{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Comparison of predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- See preprocessing of gene expression and prediction were done in CaDRReS2/pipeline/* and 03_*\n",
    "- Convert predicted delta to cv to cell death percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:12.204714Z",
     "start_time": "2020-11-19T01:24:12.193540Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy import stats\n",
    "from sklearn import metrics\n",
    "from collections import Counter\n",
    "\n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style('ticks')\n",
    "\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "pd.set_option('precision', 2)\n",
    "np.set_printoptions(suppress=True)\n",
    "from IPython.display import HTML, display"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:12.209980Z",
     "start_time": "2020-11-19T01:24:12.206681Z"
    }
   },
   "outputs": [],
   "source": [
    "import matplotlib as mpl\n",
    "mpl.rcParams['figure.dpi']= 120\n",
    "mpl.rc(\"savefig\", dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:12.215344Z",
     "start_time": "2020-11-19T01:24:12.212346Z"
    }
   },
   "outputs": [],
   "source": [
    "dosage_shifted = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for experimental validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:12.221064Z",
     "start_time": "2020-11-19T01:24:12.217300Z"
    }
   },
   "outputs": [],
   "source": [
    "dosage_used = '3 fold' # All for HN, '9 fold' '3 fold' 'Median IC50' "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "for calculating % cell death"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:12.226701Z",
     "start_time": "2020-11-19T01:24:12.223011Z"
    }
   },
   "outputs": [],
   "source": [
    "dosage_ref = 'log2_median_ic50_hn' # log2_median_ic50_hn | log2_median_ic50_3f_hn\n",
    "model_name = 'hn_drug_cw_dw10_100000_model' # hn_drug_cw_dw10_100000_model | hn_drug_cw_dw1_100000_model | hn_drug_cw_dwsim10_100000_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:12.232328Z",
     "start_time": "2020-11-19T01:24:12.229034Z"
    }
   },
   "outputs": [],
   "source": [
    "current_dir = '../result/HN_model/TPM/'\n",
    "\n",
    "# current_dir = '../result/HN_model/TMM/'\n",
    "# current_dir = '../result/HN_model/TMM_p95/'\n",
    "# current_dir = '../result/HN_model/mat_norm/'\n",
    "# current_dir = '../result/HN_model/mat_norm_p95/'\n",
    "# current_dir = '../result/HN_model/mat_norm_log2_p95/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:12.253321Z",
     "start_time": "2020-11-19T01:24:12.234195Z"
    }
   },
   "outputs": [],
   "source": [
    "if dosage_shifted:\n",
    "    pred_single_df = pd.read_csv(current_dir + 'pred_drug_kill_{}_{}_shifted.csv'.format(dosage_ref, model_name))\n",
    "    pred_combi_df = pd.read_csv(current_dir + 'pred_combi_kill_{}_{}_shifted.csv'.format(dosage_ref, model_name))\n",
    "else:\n",
    "    pred_single_df = pd.read_csv(current_dir + 'pred_drug_kill_{}_{}.csv'.format(dosage_ref, model_name))\n",
    "    pred_combi_df = pd.read_csv(current_dir + 'pred_combi_kill_{}_{}.csv'.format(dosage_ref, model_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:12.259414Z",
     "start_time": "2020-11-19T01:24:12.255925Z"
    }
   },
   "outputs": [],
   "source": [
    "# patient_list = ['HN120', 'HN137', 'HN148', 'HN159', 'HN160', 'HN182']\n",
    "patient_list = ['HN120', 'HN137', 'HN148', 'HN159', 'HN160']\n",
    "\n",
    "# single_drug_id_list = [1032, 1007, 133, 201, 1010] + [182, 301, 302] + [1012]\n",
    "# single_drug_list = ['Afatinib', 'Docetaxel', 'Doxorubicin', 'Epothilone B', 'Gefitinib'] + ['Obatoclax Mesylate', 'PHA-793887', 'PI-103'] + ['Vorinostat']\n",
    "# combi_drug_list = ['Docetaxel|Afatinib', 'Docetaxel|Epothilone B', 'Docetaxel|Gefitinib', 'Epothilone B|Afatinib', 'Gefitinib|Afatinib', 'Gefitinib|Epothilone B'] + ['Afatinib|Obatoclax Mesylate', 'Epothilone B|PI-103'] + ['Doxorubicin|Vorinostat']\n",
    "\n",
    "single_drug_id_list = [1007, 133, 201, 1010] + [182, 301, 302] + [1012]\n",
    "single_drug_list = ['Docetaxel', 'Doxorubicin', 'Epothilone B', 'Gefitinib'] + ['Obatoclax Mesylate', 'PHA-793887', 'PI-103'] + ['Vorinostat']\n",
    "combi_drug_list = ['Docetaxel|Epothilone B', 'Docetaxel|Gefitinib', 'Gefitinib|Epothilone B'] + ['Epothilone B|PI-103'] + ['Doxorubicin|Vorinostat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:12.270647Z",
     "start_time": "2020-11-19T01:24:12.261680Z"
    }
   },
   "outputs": [],
   "source": [
    "# read reference dosages file\n",
    "dosage_df = pd.read_csv('../preprocessed_data/GDSC/hn_drug_stat.csv', index_col=0)\n",
    "dosage_df = dosage_df.loc[single_drug_id_list]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####  Read experimental data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:12.469136Z",
     "start_time": "2020-11-19T01:24:12.272130Z"
    },
    "code_folding": [],
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>File name</th>\n",
       "      <th>Dosage</th>\n",
       "      <th>Drug</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "      <th>HN182</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Afatinib</td>\n",
       "      <td>1</td>\n",
       "      <td>56.98</td>\n",
       "      <td>64.74</td>\n",
       "      <td>40.52</td>\n",
       "      <td>26.77</td>\n",
       "      <td>97.41</td>\n",
       "      <td>67.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Afatinib</td>\n",
       "      <td>2</td>\n",
       "      <td>58.10</td>\n",
       "      <td>64.20</td>\n",
       "      <td>38.30</td>\n",
       "      <td>25.42</td>\n",
       "      <td>96.90</td>\n",
       "      <td>68.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Afatinib</td>\n",
       "      <td>3</td>\n",
       "      <td>53.09</td>\n",
       "      <td>59.52</td>\n",
       "      <td>35.23</td>\n",
       "      <td>24.12</td>\n",
       "      <td>93.86</td>\n",
       "      <td>66.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1</td>\n",
       "      <td>81.52</td>\n",
       "      <td>71.17</td>\n",
       "      <td>84.23</td>\n",
       "      <td>86.19</td>\n",
       "      <td>88.00</td>\n",
       "      <td>67.97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>validation_triplicate_2019_09_13.xlsx</td>\n",
       "      <td>3 fold</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>2</td>\n",
       "      <td>80.69</td>\n",
       "      <td>67.84</td>\n",
       "      <td>78.09</td>\n",
       "      <td>81.28</td>\n",
       "      <td>86.22</td>\n",
       "      <td>70.59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               File name  Dosage       Drug  Replicate  HN120  \\\n",
       "0  validation_triplicate_2019_09_13.xlsx  3 fold   Afatinib          1  56.98   \n",
       "1  validation_triplicate_2019_09_13.xlsx  3 fold   Afatinib          2  58.10   \n",
       "2  validation_triplicate_2019_09_13.xlsx  3 fold   Afatinib          3  53.09   \n",
       "6  validation_triplicate_2019_09_13.xlsx  3 fold  Docetaxel          1  81.52   \n",
       "7  validation_triplicate_2019_09_13.xlsx  3 fold  Docetaxel          2  80.69   \n",
       "\n",
       "   HN137  HN148  HN159  HN160  HN182  \n",
       "0  64.74  40.52  26.77  97.41  67.09  \n",
       "1  64.20  38.30  25.42  96.90  68.63  \n",
       "2  59.52  35.23  24.12  93.86  66.52  \n",
       "6  71.17  84.23  86.19  88.00  67.97  \n",
       "7  67.84  78.09  81.28  86.22  70.59  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##### Combined replicate #####\n",
    "\n",
    "validation_fname_list = ['../result/validation/validation_triplicate_2019_09_13.xlsx', '../result/validation/validation_replicates_2019_06_24.xlsx']\n",
    "\n",
    "cv_single_df_list = []\n",
    "cv_combi_df_list = []\n",
    "\n",
    "for validation_fname in validation_fname_list:\n",
    "\n",
    "    cv_single_df = pd.read_excel(validation_fname, sheet_name='cv_single', index_col=[0,1,2])\n",
    "    cv_single_df.loc[:, 'File name'] = validation_fname.split('/')[-1]\n",
    "    cv_single_df = cv_single_df.reset_index().groupby(['File name', 'Dosage', 'Drug', 'Replicate']).median().reset_index() # just to rearrange columns\n",
    "\n",
    "    cv_combi_df = pd.read_excel(validation_fname, sheet_name='cv_combi', index_col=[0,1,2])\n",
    "    cv_combi_df.loc[:, 'File name'] = validation_fname.split('/')[-1]\n",
    "    cv_combi_df = cv_combi_df.reset_index().groupby(['File name', 'Dosage', 'Drug', 'Replicate']).median().reset_index()\n",
    "    \n",
    "    cv_single_df_list += [cv_single_df]\n",
    "    cv_combi_df_list += [cv_combi_df]\n",
    "\n",
    "cv_df = pd.concat(cv_single_df_list + cv_combi_df_list, axis=0)\n",
    "cv_df = cv_df[~cv_df['Drug'].isin(['DMSO', 'Staurosporin'])]\n",
    "cv_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:12.483991Z",
     "start_time": "2020-11-19T01:24:12.471191Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>File name</th>\n",
       "      <th>Dosage</th>\n",
       "      <th>Drug</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "      <th>HN182</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>Median IC50</td>\n",
       "      <td>Afatinib|Obatoclax Mesylate</td>\n",
       "      <td>2</td>\n",
       "      <td>22.71</td>\n",
       "      <td>32.61</td>\n",
       "      <td>11.69</td>\n",
       "      <td>14.09</td>\n",
       "      <td>67.42</td>\n",
       "      <td>26.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>Median IC50</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>1</td>\n",
       "      <td>13.54</td>\n",
       "      <td>5.65</td>\n",
       "      <td>9.36</td>\n",
       "      <td>7.59</td>\n",
       "      <td>32.98</td>\n",
       "      <td>17.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>Median IC50</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>2</td>\n",
       "      <td>14.10</td>\n",
       "      <td>5.68</td>\n",
       "      <td>8.73</td>\n",
       "      <td>7.46</td>\n",
       "      <td>34.33</td>\n",
       "      <td>16.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>Median IC50</td>\n",
       "      <td>PHA-793887|Trametinib</td>\n",
       "      <td>1</td>\n",
       "      <td>5.03</td>\n",
       "      <td>16.88</td>\n",
       "      <td>15.93</td>\n",
       "      <td>16.35</td>\n",
       "      <td>26.12</td>\n",
       "      <td>10.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>Median IC50</td>\n",
       "      <td>PHA-793887|Trametinib</td>\n",
       "      <td>2</td>\n",
       "      <td>4.14</td>\n",
       "      <td>15.13</td>\n",
       "      <td>11.70</td>\n",
       "      <td>15.93</td>\n",
       "      <td>25.38</td>\n",
       "      <td>9.85</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                File name       Dosage  \\\n",
       "25  validation_replicates_2019_06_24.xlsx  Median IC50   \n",
       "26  validation_replicates_2019_06_24.xlsx  Median IC50   \n",
       "27  validation_replicates_2019_06_24.xlsx  Median IC50   \n",
       "28  validation_replicates_2019_06_24.xlsx  Median IC50   \n",
       "29  validation_replicates_2019_06_24.xlsx  Median IC50   \n",
       "\n",
       "                           Drug  Replicate  HN120  HN137  HN148  HN159  HN160  \\\n",
       "25  Afatinib|Obatoclax Mesylate          2  22.71  32.61  11.69  14.09  67.42   \n",
       "26          Epothilone B|PI-103          1  13.54   5.65   9.36   7.59  32.98   \n",
       "27          Epothilone B|PI-103          2  14.10   5.68   8.73   7.46  34.33   \n",
       "28        PHA-793887|Trametinib          1   5.03  16.88  15.93  16.35  26.12   \n",
       "29        PHA-793887|Trametinib          2   4.14  15.13  11.70  15.93  25.38   \n",
       "\n",
       "    HN182  \n",
       "25  26.53  \n",
       "26  17.34  \n",
       "27  16.63  \n",
       "28  10.35  \n",
       "29   9.85  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:12.491654Z",
     "start_time": "2020-11-19T01:24:12.486078Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "#### Check consistency across triplicate #####\n",
    "\n",
    "# validation_fname = validation_fname_list[0]\n",
    "\n",
    "# cv_new_df = pd.concat([pd.read_excel(validation_fname, sheet_name='cv_single', index_col=[0,1,2]), pd.read_excel(validation_fname, sheet_name='cv_combi', index_col=[0,1,2])], axis=0).reset_index()\n",
    "# cv_new_df = cv_new_df[~cv_new_df['Drug'].isin(['DMSO', 'Staurosporin'])]\n",
    "# cv_new_df = cv_new_df.set_index(['Drug', 'Dosage', 'Replicate']).stack().reset_index()\n",
    "# cv_new_df.columns = ['Drug', 'Dosage', 'Replicate', 'Patient', 'Viability']\n",
    "# cv_new_df = cv_new_df.groupby(['Dosage', 'Drug', 'Patient', 'Replicate']).sum().unstack()\n",
    "\n",
    "# sns.pairplot(cv_new_df, plot_kws={'s':10, 'alpha':0.75, 'linewidth':0}, diag_kws={'bins':20}, aspect=1.15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate % cell death at specific dosages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:12.504265Z",
     "start_time": "2020-11-19T01:24:12.493647Z"
    }
   },
   "outputs": [],
   "source": [
    "obs_kill_df = 100 - cv_df.set_index(['Dosage', 'Drug', 'File name', 'Replicate']).astype(float)\n",
    "obs_kill_df = obs_kill_df.loc[dosage_used]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:12.522546Z",
     "start_time": "2020-11-19T01:24:12.507468Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "      <th>HN182</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug</th>\n",
       "      <th>File name</th>\n",
       "      <th>Replicate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Afatinib</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">validation_triplicate_2019_09_13.xlsx</th>\n",
       "      <th>1</th>\n",
       "      <td>43.02</td>\n",
       "      <td>35.26</td>\n",
       "      <td>59.48</td>\n",
       "      <td>73.23</td>\n",
       "      <td>2.59</td>\n",
       "      <td>32.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41.90</td>\n",
       "      <td>35.80</td>\n",
       "      <td>61.70</td>\n",
       "      <td>74.58</td>\n",
       "      <td>3.10</td>\n",
       "      <td>31.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46.91</td>\n",
       "      <td>40.48</td>\n",
       "      <td>64.77</td>\n",
       "      <td>75.88</td>\n",
       "      <td>6.14</td>\n",
       "      <td>33.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Docetaxel</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">validation_triplicate_2019_09_13.xlsx</th>\n",
       "      <th>1</th>\n",
       "      <td>18.48</td>\n",
       "      <td>28.83</td>\n",
       "      <td>15.77</td>\n",
       "      <td>13.81</td>\n",
       "      <td>12.00</td>\n",
       "      <td>32.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.31</td>\n",
       "      <td>32.16</td>\n",
       "      <td>21.91</td>\n",
       "      <td>18.72</td>\n",
       "      <td>13.78</td>\n",
       "      <td>29.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.69</td>\n",
       "      <td>38.61</td>\n",
       "      <td>24.50</td>\n",
       "      <td>24.83</td>\n",
       "      <td>14.27</td>\n",
       "      <td>28.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Doxorubicin</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">validation_triplicate_2019_09_13.xlsx</th>\n",
       "      <th>1</th>\n",
       "      <td>33.32</td>\n",
       "      <td>42.17</td>\n",
       "      <td>23.79</td>\n",
       "      <td>15.58</td>\n",
       "      <td>14.64</td>\n",
       "      <td>45.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>35.72</td>\n",
       "      <td>46.45</td>\n",
       "      <td>25.15</td>\n",
       "      <td>22.23</td>\n",
       "      <td>13.93</td>\n",
       "      <td>41.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>39.04</td>\n",
       "      <td>52.38</td>\n",
       "      <td>31.03</td>\n",
       "      <td>25.80</td>\n",
       "      <td>17.89</td>\n",
       "      <td>45.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epothilone B</th>\n",
       "      <th>validation_triplicate_2019_09_13.xlsx</th>\n",
       "      <th>1</th>\n",
       "      <td>50.24</td>\n",
       "      <td>60.62</td>\n",
       "      <td>30.33</td>\n",
       "      <td>31.60</td>\n",
       "      <td>28.97</td>\n",
       "      <td>55.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                              HN120  HN137  \\\n",
       "Drug         File name                             Replicate                 \n",
       "Afatinib     validation_triplicate_2019_09_13.xlsx 1          43.02  35.26   \n",
       "                                                   2          41.90  35.80   \n",
       "                                                   3          46.91  40.48   \n",
       "Docetaxel    validation_triplicate_2019_09_13.xlsx 1          18.48  28.83   \n",
       "                                                   2          19.31  32.16   \n",
       "                                                   3          23.69  38.61   \n",
       "Doxorubicin  validation_triplicate_2019_09_13.xlsx 1          33.32  42.17   \n",
       "                                                   2          35.72  46.45   \n",
       "                                                   3          39.04  52.38   \n",
       "Epothilone B validation_triplicate_2019_09_13.xlsx 1          50.24  60.62   \n",
       "\n",
       "                                                              HN148  HN159  \\\n",
       "Drug         File name                             Replicate                 \n",
       "Afatinib     validation_triplicate_2019_09_13.xlsx 1          59.48  73.23   \n",
       "                                                   2          61.70  74.58   \n",
       "                                                   3          64.77  75.88   \n",
       "Docetaxel    validation_triplicate_2019_09_13.xlsx 1          15.77  13.81   \n",
       "                                                   2          21.91  18.72   \n",
       "                                                   3          24.50  24.83   \n",
       "Doxorubicin  validation_triplicate_2019_09_13.xlsx 1          23.79  15.58   \n",
       "                                                   2          25.15  22.23   \n",
       "                                                   3          31.03  25.80   \n",
       "Epothilone B validation_triplicate_2019_09_13.xlsx 1          30.33  31.60   \n",
       "\n",
       "                                                              HN160  HN182  \n",
       "Drug         File name                             Replicate                \n",
       "Afatinib     validation_triplicate_2019_09_13.xlsx 1           2.59  32.91  \n",
       "                                                   2           3.10  31.37  \n",
       "                                                   3           6.14  33.48  \n",
       "Docetaxel    validation_triplicate_2019_09_13.xlsx 1          12.00  32.03  \n",
       "                                                   2          13.78  29.41  \n",
       "                                                   3          14.27  28.43  \n",
       "Doxorubicin  validation_triplicate_2019_09_13.xlsx 1          14.64  45.80  \n",
       "                                                   2          13.93  41.51  \n",
       "                                                   3          17.89  45.25  \n",
       "Epothilone B validation_triplicate_2019_09_13.xlsx 1          28.97  55.86  "
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_kill_df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:12.546520Z",
     "start_time": "2020-11-19T01:24:12.524097Z"
    }
   },
   "outputs": [],
   "source": [
    "pred_single_df = pred_single_df[pred_single_df['drug_id'].isin(single_drug_id_list)]\n",
    "\n",
    "pred_combi_df = pred_combi_df[pred_combi_df['drug_id_A'].isin(single_drug_id_list) & pred_combi_df['drug_id_B'].isin(single_drug_id_list)]\n",
    "\n",
    "pred_combi_df.loc[:, 'Combi Name 1'] = pred_combi_df['drug_name_A'].values + '|' + pred_combi_df['drug_name_B'].values\n",
    "pred_combi_df.loc[:, 'Combi Name 2'] = pred_combi_df['drug_name_B'].values + '|' + pred_combi_df['drug_name_A'].values\n",
    "\n",
    "temp1 = pred_combi_df['Combi Name 1'][pred_combi_df['Combi Name 1'].isin(combi_drug_list)]\n",
    "temp2 = pred_combi_df['Combi Name 2'][pred_combi_df['Combi Name 2'].isin(combi_drug_list)]\n",
    "combi_name = pd.concat([temp1, temp2]).values\n",
    "\n",
    "pred_combi_df = pd.concat([pred_combi_df[pred_combi_df['Combi Name 1'].isin(combi_drug_list)], pred_combi_df[pred_combi_df['Combi Name 2'].isin(combi_drug_list)]])\n",
    "pred_combi_df.loc[:, 'Combi Name'] = combi_name\n",
    "pred_combi_df = pred_combi_df[pred_combi_df['Combi Name'].isin(combi_drug_list)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:12.564594Z",
     "start_time": "2020-11-19T01:24:12.549682Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>drug_id</th>\n",
       "      <th>cluster</th>\n",
       "      <th>cluster_p</th>\n",
       "      <th>cluster_delta</th>\n",
       "      <th>delta</th>\n",
       "      <th>cluster_kill</th>\n",
       "      <th>kill</th>\n",
       "      <th>drug_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1007</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>3.0508057471845|2.68344052681|4.0167768343325|...</td>\n",
       "      <td>3.26</td>\n",
       "      <td>10.768034058802|13.470080993016|5.818301286009...</td>\n",
       "      <td>8.52</td>\n",
       "      <td>Docetaxel</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1010</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>2.2212500077716|2.0557795051645|2.677399181711...</td>\n",
       "      <td>2.28</td>\n",
       "      <td>17.658569782699|19.388553836629|13.51896421525...</td>\n",
       "      <td>15.31</td>\n",
       "      <td>Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1012</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>-0.18193634608133|-0.10973613232891|-0.2523313...</td>\n",
       "      <td>-0.21</td>\n",
       "      <td>53.148545025719|51.900665978329|54.36145585765...</td>\n",
       "      <td>51.07</td>\n",
       "      <td>Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>HN120</td>\n",
       "      <td>133</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>-2.7850163000908|-2.4017062802293|-1.270422554...</td>\n",
       "      <td>-1.95</td>\n",
       "      <td>87.32965858477|84.087272226485|70.694442969728...</td>\n",
       "      <td>75.63</td>\n",
       "      <td>Doxorubicin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>HN120</td>\n",
       "      <td>182</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>-1.8025358332495|-1.1619028613393|-0.565126429...</td>\n",
       "      <td>-1.07</td>\n",
       "      <td>77.719989977921|69.112193712182|59.66956727385...</td>\n",
       "      <td>64.66</td>\n",
       "      <td>Obatoclax Mesylate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   patient  drug_id      cluster  \\\n",
       "4    HN120     1007  D1|D2|G1|G2   \n",
       "5    HN120     1010  D1|D2|G1|G2   \n",
       "6    HN120     1012  D1|D2|G1|G2   \n",
       "24   HN120      133  D1|D2|G1|G2   \n",
       "44   HN120      182  D1|D2|G1|G2   \n",
       "\n",
       "                                            cluster_p  \\\n",
       "4   0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "5   0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "6   0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "24  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "44  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "\n",
       "                                        cluster_delta  delta  \\\n",
       "4   3.0508057471845|2.68344052681|4.0167768343325|...   3.26   \n",
       "5   2.2212500077716|2.0557795051645|2.677399181711...   2.28   \n",
       "6   -0.18193634608133|-0.10973613232891|-0.2523313...  -0.21   \n",
       "24  -2.7850163000908|-2.4017062802293|-1.270422554...  -1.95   \n",
       "44  -1.8025358332495|-1.1619028613393|-0.565126429...  -1.07   \n",
       "\n",
       "                                         cluster_kill   kill  \\\n",
       "4   10.768034058802|13.470080993016|5.818301286009...   8.52   \n",
       "5   17.658569782699|19.388553836629|13.51896421525...  15.31   \n",
       "6   53.148545025719|51.900665978329|54.36145585765...  51.07   \n",
       "24  87.32965858477|84.087272226485|70.694442969728...  75.63   \n",
       "44  77.719989977921|69.112193712182|59.66956727385...  64.66   \n",
       "\n",
       "             drug_name  \n",
       "4            Docetaxel  \n",
       "5            Gefitinib  \n",
       "6           Vorinostat  \n",
       "24         Doxorubicin  \n",
       "44  Obatoclax Mesylate  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_single_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:12.602101Z",
     "start_time": "2020-11-19T01:24:12.566282Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient</th>\n",
       "      <th>drug_id_A</th>\n",
       "      <th>drug_name_A</th>\n",
       "      <th>drug_id_B</th>\n",
       "      <th>drug_name_B</th>\n",
       "      <th>cluster</th>\n",
       "      <th>cluster_p</th>\n",
       "      <th>cluster_kill_A</th>\n",
       "      <th>cluster_kill_B</th>\n",
       "      <th>cluster_kill_C</th>\n",
       "      <th>kill_A</th>\n",
       "      <th>kill_B</th>\n",
       "      <th>kill_C</th>\n",
       "      <th>improve</th>\n",
       "      <th>improve_p</th>\n",
       "      <th>kill_entropy</th>\n",
       "      <th>sum_kill_dif</th>\n",
       "      <th>Combi Name 1</th>\n",
       "      <th>Combi Name 2</th>\n",
       "      <th>Combi Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>HN137</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>E1|E2|E3|F1|F2|F3</td>\n",
       "      <td>0.34090909090909|0.085227272727273|0.073863636...</td>\n",
       "      <td>62.407833255845|16.012617329511|38.76244513162...</td>\n",
       "      <td>51.674841534449|23.592369767377|61.04708071736...</td>\n",
       "      <td>81.833525850253|35.827231207075|76.14618468146...</td>\n",
       "      <td>46.72</td>\n",
       "      <td>53.50</td>\n",
       "      <td>73.27</td>\n",
       "      <td>19.78</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.94</td>\n",
       "      <td>108.15</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>HN120</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>82.559569253274|75.901345747413|42.11102158023...</td>\n",
       "      <td>73.319982827324|81.138919196998|93.555298867|8...</td>\n",
       "      <td>95.346890081785|95.454733348983|96.26922835189...</td>\n",
       "      <td>60.39</td>\n",
       "      <td>79.82</td>\n",
       "      <td>90.94</td>\n",
       "      <td>11.11</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.94</td>\n",
       "      <td>98.02</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>HN159</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>I1|I2|K1</td>\n",
       "      <td>0.31736526946108|0.18562874251497|0.4850299401...</td>\n",
       "      <td>9.8488162520195|6.8643188643582|7.5176665757013</td>\n",
       "      <td>4.5783295305561|26.712006978304|62.386661664547</td>\n",
       "      <td>13.976234519699|31.742728508602|65.214307028597</td>\n",
       "      <td>8.05</td>\n",
       "      <td>36.67</td>\n",
       "      <td>41.96</td>\n",
       "      <td>5.29</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.91</td>\n",
       "      <td>79.99</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>HN159</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>I1|I2|K1</td>\n",
       "      <td>0.31736526946108|0.18562874251497|0.4850299401...</td>\n",
       "      <td>4.5783295305561|26.712006978304|62.386661664547</td>\n",
       "      <td>17.277397531198|11.231238406469|8.9765429170035</td>\n",
       "      <td>21.064710868472|34.943156197887|65.763039122747</td>\n",
       "      <td>36.67</td>\n",
       "      <td>11.92</td>\n",
       "      <td>45.07</td>\n",
       "      <td>8.40</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.91</td>\n",
       "      <td>81.59</td>\n",
       "      <td>Epothilone B|Gefitinib</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>HN159</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>I1|I2|K1</td>\n",
       "      <td>0.31736526946108|0.18562874251497|0.4850299401...</td>\n",
       "      <td>28.983081352912|50.03661556134|76.983586526061</td>\n",
       "      <td>31.76964520609|28.320074354813|28.979206612439</td>\n",
       "      <td>51.544904443389|64.18628318455|83.653560541447</td>\n",
       "      <td>55.83</td>\n",
       "      <td>29.40</td>\n",
       "      <td>68.85</td>\n",
       "      <td>13.02</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.91</td>\n",
       "      <td>72.51</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>HN137</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>E1|E2|E3|F1|F2|F3</td>\n",
       "      <td>0.34090909090909|0.085227272727273|0.073863636...</td>\n",
       "      <td>19.585333371545|16.02760069062|12.861596918384...</td>\n",
       "      <td>22.785538571641|17.573094960434|25.58024748614...</td>\n",
       "      <td>37.908248253428|30.784150161812|35.15181608213...</td>\n",
       "      <td>14.76</td>\n",
       "      <td>16.63</td>\n",
       "      <td>28.70</td>\n",
       "      <td>12.08</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.85</td>\n",
       "      <td>27.16</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>HN160</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>B1|B2|L</td>\n",
       "      <td>0.42222222222222|0.41481481481481|0.1629629629...</td>\n",
       "      <td>21.126704027831|18.684548520554|47.671758024425</td>\n",
       "      <td>29.541600387792|34.82325891846|37.417736452227</td>\n",
       "      <td>44.42713793661|47.001238729956|67.251801696929</td>\n",
       "      <td>24.44</td>\n",
       "      <td>33.02</td>\n",
       "      <td>49.21</td>\n",
       "      <td>16.20</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.64</td>\n",
       "      <td>34.81</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>HN160</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>B1|B2|L</td>\n",
       "      <td>0.42222222222222|0.41481481481481|0.1629629629...</td>\n",
       "      <td>5.0622043413293|4.2426253055942|24.796381150615</td>\n",
       "      <td>14.912799872087|17.187718708948|11.757532700462</td>\n",
       "      <td>19.220087810878|20.701133511142|33.638471228762</td>\n",
       "      <td>7.94</td>\n",
       "      <td>15.34</td>\n",
       "      <td>22.18</td>\n",
       "      <td>6.84</td>\n",
       "      <td>0.45</td>\n",
       "      <td>0.64</td>\n",
       "      <td>35.83</td>\n",
       "      <td>Epothilone B|Gefitinib</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>HN160</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>B1|B2|L</td>\n",
       "      <td>0.42222222222222|0.41481481481481|0.1629629629...</td>\n",
       "      <td>7.8138993830314|12.826479425884|6.3155137520376</td>\n",
       "      <td>5.0622043413293|4.2426253055942|24.796381150615</td>\n",
       "      <td>12.480548170566|16.524925269539|29.545876041078</td>\n",
       "      <td>9.65</td>\n",
       "      <td>7.94</td>\n",
       "      <td>16.94</td>\n",
       "      <td>7.29</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.64</td>\n",
       "      <td>29.82</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>HN137</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>E1|E2|E3|F1|F2|F3</td>\n",
       "      <td>0.34090909090909|0.085227272727273|0.073863636...</td>\n",
       "      <td>62.407833255845|16.012617329511|38.76244513162...</td>\n",
       "      <td>22.785538571641|17.573094960434|25.58024748614...</td>\n",
       "      <td>70.97341090925|30.771799840979|54.42716322142|...</td>\n",
       "      <td>46.72</td>\n",
       "      <td>16.63</td>\n",
       "      <td>55.05</td>\n",
       "      <td>8.33</td>\n",
       "      <td>0.18</td>\n",
       "      <td>0.44</td>\n",
       "      <td>128.78</td>\n",
       "      <td>Epothilone B|Gefitinib</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>HN137</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>E1|E2|E3|F1|F2|F3</td>\n",
       "      <td>0.34090909090909|0.085227272727273|0.073863636...</td>\n",
       "      <td>19.585333371545|16.02760069062|12.861596918384...</td>\n",
       "      <td>62.407833255845|16.012617329511|38.76244513162...</td>\n",
       "      <td>69.770384434275|29.47377965444|46.638572601469...</td>\n",
       "      <td>14.76</td>\n",
       "      <td>46.72</td>\n",
       "      <td>53.85</td>\n",
       "      <td>7.14</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.44</td>\n",
       "      <td>145.97</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>HN182</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>J1|J2|L</td>\n",
       "      <td>0.71910112359551|0.20224719101124|0.0786516853...</td>\n",
       "      <td>8.2891102953025|7.7220448993924|47.671758024425</td>\n",
       "      <td>27.843910258139|21.427308170097|37.417736452227</td>\n",
       "      <td>33.825008121619|27.494726711863|67.251801696929</td>\n",
       "      <td>11.27</td>\n",
       "      <td>27.30</td>\n",
       "      <td>35.17</td>\n",
       "      <td>7.87</td>\n",
       "      <td>0.29</td>\n",
       "      <td>0.40</td>\n",
       "      <td>43.51</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>HN182</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>J1|J2|L</td>\n",
       "      <td>0.71910112359551|0.20224719101124|0.0786516853...</td>\n",
       "      <td>4.4791106006856|3.3150580642658|6.3155137520376</td>\n",
       "      <td>3.5943069485864|3.0879766099691|24.796381150615</td>\n",
       "      <td>7.9124245657167|6.3006664566035|29.545876041078</td>\n",
       "      <td>4.39</td>\n",
       "      <td>5.16</td>\n",
       "      <td>9.29</td>\n",
       "      <td>4.13</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.40</td>\n",
       "      <td>19.59</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>HN182</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>J1|J2|L</td>\n",
       "      <td>0.71910112359551|0.20224719101124|0.0786516853...</td>\n",
       "      <td>3.5943069485864|3.0879766099691|24.796381150615</td>\n",
       "      <td>13.723048774929|13.754347639081|11.757532700462</td>\n",
       "      <td>16.82410722784|16.417593211101|33.638471228762</td>\n",
       "      <td>5.16</td>\n",
       "      <td>13.57</td>\n",
       "      <td>18.06</td>\n",
       "      <td>4.49</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.40</td>\n",
       "      <td>33.83</td>\n",
       "      <td>Epothilone B|Gefitinib</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>HN120</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>82.559569253274|75.901345747413|42.11102158023...</td>\n",
       "      <td>17.658569782699|19.388553836629|13.51896421525...</td>\n",
       "      <td>85.639299887088|80.573726301079|49.93701185737...</td>\n",
       "      <td>60.39</td>\n",
       "      <td>15.31</td>\n",
       "      <td>65.61</td>\n",
       "      <td>5.22</td>\n",
       "      <td>0.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>191.99</td>\n",
       "      <td>Epothilone B|Gefitinib</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>HN182</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>J1|J2|L</td>\n",
       "      <td>0.71910112359551|0.20224719101124|0.0786516853...</td>\n",
       "      <td>3.5943069485864|3.0879766099691|24.796381150615</td>\n",
       "      <td>47.214535667313|57.957322824882|79.296030945375</td>\n",
       "      <td>49.111807279666|59.255590862254|84.429866025465</td>\n",
       "      <td>5.16</td>\n",
       "      <td>51.91</td>\n",
       "      <td>53.94</td>\n",
       "      <td>2.03</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>152.99</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>HN160</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>B1|B2|L</td>\n",
       "      <td>0.42222222222222|0.41481481481481|0.1629629629...</td>\n",
       "      <td>5.0622043413293|4.2426253055942|24.796381150615</td>\n",
       "      <td>82.679600360116|86.600099622362|79.296030945375</td>\n",
       "      <td>83.556394382622|87.168607186708|84.429866025465</td>\n",
       "      <td>7.94</td>\n",
       "      <td>83.75</td>\n",
       "      <td>85.20</td>\n",
       "      <td>1.44</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>214.47</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>HN148</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>C1|C2|H1</td>\n",
       "      <td>0.31351351351351|0.20540540540541|0.4594594594...</td>\n",
       "      <td>12.287320856345|22.2747098326|22.405104198282</td>\n",
       "      <td>22.397943597246|26.360026344365|24.519696059614</td>\n",
       "      <td>31.933157258574|42.763116796961|41.431136806637</td>\n",
       "      <td>18.72</td>\n",
       "      <td>23.70</td>\n",
       "      <td>37.83</td>\n",
       "      <td>14.13</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.31</td>\n",
       "      <td>Epothilone B|Gefitinib</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>HN182</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>J1|J2|L</td>\n",
       "      <td>0.71910112359551|0.20224719101124|0.0786516853...</td>\n",
       "      <td>4.4791106006856|3.3150580642658|6.3155137520376</td>\n",
       "      <td>13.723048774929|13.754347639081|11.757532700462</td>\n",
       "      <td>17.587488843199|16.61344109275|17.330497857902</td>\n",
       "      <td>4.39</td>\n",
       "      <td>13.57</td>\n",
       "      <td>17.37</td>\n",
       "      <td>3.80</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.00</td>\n",
       "      <td>25.13</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>10.768034058802|13.470080993016|5.818301286009...</td>\n",
       "      <td>82.559569253274|75.901345747413|42.11102158023...</td>\n",
       "      <td>84.437560776083|79.147453993463|45.47917675608...</td>\n",
       "      <td>8.52</td>\n",
       "      <td>60.39</td>\n",
       "      <td>63.04</td>\n",
       "      <td>2.65</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>220.52</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>154</th>\n",
       "      <td>HN160</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>B1|B2|L</td>\n",
       "      <td>0.42222222222222|0.41481481481481|0.1629629629...</td>\n",
       "      <td>7.8138993830314|12.826479425884|6.3155137520376</td>\n",
       "      <td>14.912799872087|17.187718708948|11.757532700462</td>\n",
       "      <td>21.561428077921|27.80961893085|17.330497857902</td>\n",
       "      <td>9.65</td>\n",
       "      <td>15.34</td>\n",
       "      <td>23.46</td>\n",
       "      <td>8.12</td>\n",
       "      <td>0.53</td>\n",
       "      <td>0.00</td>\n",
       "      <td>16.90</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HN120</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>10.768034058802|13.470080993016|5.818301286009...</td>\n",
       "      <td>17.658569782699|19.388553836629|13.51896421525...</td>\n",
       "      <td>26.525123033003|30.246980924477|18.55069143247...</td>\n",
       "      <td>8.52</td>\n",
       "      <td>15.31</td>\n",
       "      <td>22.39</td>\n",
       "      <td>7.08</td>\n",
       "      <td>0.46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>28.53</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>HN159</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>I1|I2|K1</td>\n",
       "      <td>0.31736526946108|0.18562874251497|0.4850299401...</td>\n",
       "      <td>9.8488162520195|6.8643188643582|7.5176665757013</td>\n",
       "      <td>17.277397531198|11.231238406469|8.9765429170035</td>\n",
       "      <td>25.424594647239|17.324609254191|15.81938292618</td>\n",
       "      <td>8.05</td>\n",
       "      <td>11.92</td>\n",
       "      <td>18.96</td>\n",
       "      <td>7.04</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13.25</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>HN148</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>C1|C2|H1</td>\n",
       "      <td>0.31351351351351|0.20540540540541|0.4594594594...</td>\n",
       "      <td>12.287320856345|22.2747098326|22.405104198282</td>\n",
       "      <td>72.859770363413|61.355948301062|62.508486128964</td>\n",
       "      <td>76.194577460009|69.96379868456|70.908498877283</td>\n",
       "      <td>18.72</td>\n",
       "      <td>64.17</td>\n",
       "      <td>70.84</td>\n",
       "      <td>6.67</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "      <td>139.76</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>HN148</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>C1|C2|H1</td>\n",
       "      <td>0.31351351351351|0.20540540540541|0.4594594594...</td>\n",
       "      <td>49.659553735325|42.013415558165|44.673842820681</td>\n",
       "      <td>45.105443157897|39.9119241788|39.590219241212</td>\n",
       "      <td>72.36583511067|65.156977174466|66.577589745711</td>\n",
       "      <td>44.72</td>\n",
       "      <td>40.53</td>\n",
       "      <td>66.66</td>\n",
       "      <td>21.94</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>11.74</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>HN148</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>1010</td>\n",
       "      <td>Gefitinib</td>\n",
       "      <td>C1|C2|H1</td>\n",
       "      <td>0.31351351351351|0.20540540540541|0.4594594594...</td>\n",
       "      <td>8.5251501618717|8.713636383848|5.6229297182764</td>\n",
       "      <td>22.397943597246|26.360026344365|24.519696059614</td>\n",
       "      <td>29.013635434281|32.776745881878|28.763900501323</td>\n",
       "      <td>7.05</td>\n",
       "      <td>23.70</td>\n",
       "      <td>29.04</td>\n",
       "      <td>5.34</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.00</td>\n",
       "      <td>50.42</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>Gefitinib|Docetaxel</td>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>HN148</td>\n",
       "      <td>1007</td>\n",
       "      <td>Docetaxel</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>C1|C2|H1</td>\n",
       "      <td>0.31351351351351|0.20540540540541|0.4594594594...</td>\n",
       "      <td>8.5251501618717|8.713636383848|5.6229297182764</td>\n",
       "      <td>12.287320856345|22.2747098326|22.405104198282</td>\n",
       "      <td>19.764958464342|29.047408996078|26.768210654182</td>\n",
       "      <td>7.05</td>\n",
       "      <td>18.72</td>\n",
       "      <td>24.46</td>\n",
       "      <td>5.74</td>\n",
       "      <td>0.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>34.11</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>Epothilone B|Docetaxel</td>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>HN137</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>E1|E2|E3|F1|F2|F3</td>\n",
       "      <td>0.34090909090909|0.085227272727273|0.073863636...</td>\n",
       "      <td>68.244069728685|35.34826924999|50.029643891255...</td>\n",
       "      <td>55.216153465926|32.01705858264|36.598644764046...</td>\n",
       "      <td>85.778472921827|56.047851758911|68.31811701082...</td>\n",
       "      <td>61.28</td>\n",
       "      <td>41.86</td>\n",
       "      <td>76.13</td>\n",
       "      <td>14.86</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.00</td>\n",
       "      <td>102.57</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>HN120</td>\n",
       "      <td>133</td>\n",
       "      <td>Doxorubicin</td>\n",
       "      <td>1012</td>\n",
       "      <td>Vorinostat</td>\n",
       "      <td>D1|D2|G1|G2</td>\n",
       "      <td>0.31318681318681|0.17582417582418|0.3406593406...</td>\n",
       "      <td>87.32965858477|84.087272226485|70.694442969728...</td>\n",
       "      <td>53.148545025719|51.900665978329|54.36145585765...</td>\n",
       "      <td>94.063760696756|92.346083916258|86.62537041857...</td>\n",
       "      <td>75.63</td>\n",
       "      <td>51.07</td>\n",
       "      <td>86.12</td>\n",
       "      <td>10.49</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>104.46</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>Vorinostat|Doxorubicin</td>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>HN159</td>\n",
       "      <td>201</td>\n",
       "      <td>Epothilone B</td>\n",
       "      <td>302</td>\n",
       "      <td>PI-103</td>\n",
       "      <td>I1|I2|K1</td>\n",
       "      <td>0.31736526946108|0.18562874251497|0.4850299401...</td>\n",
       "      <td>4.5783295305561|26.712006978304|62.386661664547</td>\n",
       "      <td>83.629237876796|91.714939092078|86.843750756455</td>\n",
       "      <td>84.37874531346|93.928045139959|95.05149545977</td>\n",
       "      <td>36.67</td>\n",
       "      <td>85.69</td>\n",
       "      <td>90.32</td>\n",
       "      <td>4.63</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>168.51</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>PI-103|Epothilone B</td>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    patient  drug_id_A   drug_name_A  drug_id_B   drug_name_B  \\\n",
       "60    HN137        201  Epothilone B        302        PI-103   \n",
       "24    HN120        201  Epothilone B        302        PI-103   \n",
       "117   HN159       1007     Docetaxel        201  Epothilone B   \n",
       "129   HN159        201  Epothilone B       1010     Gefitinib   \n",
       "128   HN159        133   Doxorubicin       1012    Vorinostat   \n",
       "46    HN137       1007     Docetaxel       1010     Gefitinib   \n",
       "164   HN160        133   Doxorubicin       1012    Vorinostat   \n",
       "165   HN160        201  Epothilone B       1010     Gefitinib   \n",
       "153   HN160       1007     Docetaxel        201  Epothilone B   \n",
       "57    HN137        201  Epothilone B       1010     Gefitinib   \n",
       "45    HN137       1007     Docetaxel        201  Epothilone B   \n",
       "200   HN182        133   Doxorubicin       1012    Vorinostat   \n",
       "189   HN182       1007     Docetaxel        201  Epothilone B   \n",
       "201   HN182        201  Epothilone B       1010     Gefitinib   \n",
       "21    HN120        201  Epothilone B       1010     Gefitinib   \n",
       "204   HN182        201  Epothilone B        302        PI-103   \n",
       "168   HN160        201  Epothilone B        302        PI-103   \n",
       "93    HN148        201  Epothilone B       1010     Gefitinib   \n",
       "190   HN182       1007     Docetaxel       1010     Gefitinib   \n",
       "9     HN120       1007     Docetaxel        201  Epothilone B   \n",
       "154   HN160       1007     Docetaxel       1010     Gefitinib   \n",
       "10    HN120       1007     Docetaxel       1010     Gefitinib   \n",
       "118   HN159       1007     Docetaxel       1010     Gefitinib   \n",
       "96    HN148        201  Epothilone B        302        PI-103   \n",
       "92    HN148        133   Doxorubicin       1012    Vorinostat   \n",
       "82    HN148       1007     Docetaxel       1010     Gefitinib   \n",
       "81    HN148       1007     Docetaxel        201  Epothilone B   \n",
       "56    HN137        133   Doxorubicin       1012    Vorinostat   \n",
       "20    HN120        133   Doxorubicin       1012    Vorinostat   \n",
       "132   HN159        201  Epothilone B        302        PI-103   \n",
       "\n",
       "               cluster                                          cluster_p  \\\n",
       "60   E1|E2|E3|F1|F2|F3  0.34090909090909|0.085227272727273|0.073863636...   \n",
       "24         D1|D2|G1|G2  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "117           I1|I2|K1  0.31736526946108|0.18562874251497|0.4850299401...   \n",
       "129           I1|I2|K1  0.31736526946108|0.18562874251497|0.4850299401...   \n",
       "128           I1|I2|K1  0.31736526946108|0.18562874251497|0.4850299401...   \n",
       "46   E1|E2|E3|F1|F2|F3  0.34090909090909|0.085227272727273|0.073863636...   \n",
       "164            B1|B2|L  0.42222222222222|0.41481481481481|0.1629629629...   \n",
       "165            B1|B2|L  0.42222222222222|0.41481481481481|0.1629629629...   \n",
       "153            B1|B2|L  0.42222222222222|0.41481481481481|0.1629629629...   \n",
       "57   E1|E2|E3|F1|F2|F3  0.34090909090909|0.085227272727273|0.073863636...   \n",
       "45   E1|E2|E3|F1|F2|F3  0.34090909090909|0.085227272727273|0.073863636...   \n",
       "200            J1|J2|L  0.71910112359551|0.20224719101124|0.0786516853...   \n",
       "189            J1|J2|L  0.71910112359551|0.20224719101124|0.0786516853...   \n",
       "201            J1|J2|L  0.71910112359551|0.20224719101124|0.0786516853...   \n",
       "21         D1|D2|G1|G2  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "204            J1|J2|L  0.71910112359551|0.20224719101124|0.0786516853...   \n",
       "168            B1|B2|L  0.42222222222222|0.41481481481481|0.1629629629...   \n",
       "93            C1|C2|H1  0.31351351351351|0.20540540540541|0.4594594594...   \n",
       "190            J1|J2|L  0.71910112359551|0.20224719101124|0.0786516853...   \n",
       "9          D1|D2|G1|G2  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "154            B1|B2|L  0.42222222222222|0.41481481481481|0.1629629629...   \n",
       "10         D1|D2|G1|G2  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "118           I1|I2|K1  0.31736526946108|0.18562874251497|0.4850299401...   \n",
       "96            C1|C2|H1  0.31351351351351|0.20540540540541|0.4594594594...   \n",
       "92            C1|C2|H1  0.31351351351351|0.20540540540541|0.4594594594...   \n",
       "82            C1|C2|H1  0.31351351351351|0.20540540540541|0.4594594594...   \n",
       "81            C1|C2|H1  0.31351351351351|0.20540540540541|0.4594594594...   \n",
       "56   E1|E2|E3|F1|F2|F3  0.34090909090909|0.085227272727273|0.073863636...   \n",
       "20         D1|D2|G1|G2  0.31318681318681|0.17582417582418|0.3406593406...   \n",
       "132           I1|I2|K1  0.31736526946108|0.18562874251497|0.4850299401...   \n",
       "\n",
       "                                        cluster_kill_A  \\\n",
       "60   62.407833255845|16.012617329511|38.76244513162...   \n",
       "24   82.559569253274|75.901345747413|42.11102158023...   \n",
       "117    9.8488162520195|6.8643188643582|7.5176665757013   \n",
       "129    4.5783295305561|26.712006978304|62.386661664547   \n",
       "128     28.983081352912|50.03661556134|76.983586526061   \n",
       "46   19.585333371545|16.02760069062|12.861596918384...   \n",
       "164    21.126704027831|18.684548520554|47.671758024425   \n",
       "165    5.0622043413293|4.2426253055942|24.796381150615   \n",
       "153    7.8138993830314|12.826479425884|6.3155137520376   \n",
       "57   62.407833255845|16.012617329511|38.76244513162...   \n",
       "45   19.585333371545|16.02760069062|12.861596918384...   \n",
       "200    8.2891102953025|7.7220448993924|47.671758024425   \n",
       "189    4.4791106006856|3.3150580642658|6.3155137520376   \n",
       "201    3.5943069485864|3.0879766099691|24.796381150615   \n",
       "21   82.559569253274|75.901345747413|42.11102158023...   \n",
       "204    3.5943069485864|3.0879766099691|24.796381150615   \n",
       "168    5.0622043413293|4.2426253055942|24.796381150615   \n",
       "93       12.287320856345|22.2747098326|22.405104198282   \n",
       "190    4.4791106006856|3.3150580642658|6.3155137520376   \n",
       "9    10.768034058802|13.470080993016|5.818301286009...   \n",
       "154    7.8138993830314|12.826479425884|6.3155137520376   \n",
       "10   10.768034058802|13.470080993016|5.818301286009...   \n",
       "118    9.8488162520195|6.8643188643582|7.5176665757013   \n",
       "96       12.287320856345|22.2747098326|22.405104198282   \n",
       "92     49.659553735325|42.013415558165|44.673842820681   \n",
       "82      8.5251501618717|8.713636383848|5.6229297182764   \n",
       "81      8.5251501618717|8.713636383848|5.6229297182764   \n",
       "56   68.244069728685|35.34826924999|50.029643891255...   \n",
       "20   87.32965858477|84.087272226485|70.694442969728...   \n",
       "132    4.5783295305561|26.712006978304|62.386661664547   \n",
       "\n",
       "                                        cluster_kill_B  \\\n",
       "60   51.674841534449|23.592369767377|61.04708071736...   \n",
       "24   73.319982827324|81.138919196998|93.555298867|8...   \n",
       "117    4.5783295305561|26.712006978304|62.386661664547   \n",
       "129    17.277397531198|11.231238406469|8.9765429170035   \n",
       "128     31.76964520609|28.320074354813|28.979206612439   \n",
       "46   22.785538571641|17.573094960434|25.58024748614...   \n",
       "164     29.541600387792|34.82325891846|37.417736452227   \n",
       "165    14.912799872087|17.187718708948|11.757532700462   \n",
       "153    5.0622043413293|4.2426253055942|24.796381150615   \n",
       "57   22.785538571641|17.573094960434|25.58024748614...   \n",
       "45   62.407833255845|16.012617329511|38.76244513162...   \n",
       "200    27.843910258139|21.427308170097|37.417736452227   \n",
       "189    3.5943069485864|3.0879766099691|24.796381150615   \n",
       "201    13.723048774929|13.754347639081|11.757532700462   \n",
       "21   17.658569782699|19.388553836629|13.51896421525...   \n",
       "204    47.214535667313|57.957322824882|79.296030945375   \n",
       "168    82.679600360116|86.600099622362|79.296030945375   \n",
       "93     22.397943597246|26.360026344365|24.519696059614   \n",
       "190    13.723048774929|13.754347639081|11.757532700462   \n",
       "9    82.559569253274|75.901345747413|42.11102158023...   \n",
       "154    14.912799872087|17.187718708948|11.757532700462   \n",
       "10   17.658569782699|19.388553836629|13.51896421525...   \n",
       "118    17.277397531198|11.231238406469|8.9765429170035   \n",
       "96     72.859770363413|61.355948301062|62.508486128964   \n",
       "92       45.105443157897|39.9119241788|39.590219241212   \n",
       "82     22.397943597246|26.360026344365|24.519696059614   \n",
       "81       12.287320856345|22.2747098326|22.405104198282   \n",
       "56   55.216153465926|32.01705858264|36.598644764046...   \n",
       "20   53.148545025719|51.900665978329|54.36145585765...   \n",
       "132    83.629237876796|91.714939092078|86.843750756455   \n",
       "\n",
       "                                        cluster_kill_C  kill_A  kill_B  \\\n",
       "60   81.833525850253|35.827231207075|76.14618468146...   46.72   53.50   \n",
       "24   95.346890081785|95.454733348983|96.26922835189...   60.39   79.82   \n",
       "117    13.976234519699|31.742728508602|65.214307028597    8.05   36.67   \n",
       "129    21.064710868472|34.943156197887|65.763039122747   36.67   11.92   \n",
       "128     51.544904443389|64.18628318455|83.653560541447   55.83   29.40   \n",
       "46   37.908248253428|30.784150161812|35.15181608213...   14.76   16.63   \n",
       "164     44.42713793661|47.001238729956|67.251801696929   24.44   33.02   \n",
       "165    19.220087810878|20.701133511142|33.638471228762    7.94   15.34   \n",
       "153    12.480548170566|16.524925269539|29.545876041078    9.65    7.94   \n",
       "57   70.97341090925|30.771799840979|54.42716322142|...   46.72   16.63   \n",
       "45   69.770384434275|29.47377965444|46.638572601469...   14.76   46.72   \n",
       "200    33.825008121619|27.494726711863|67.251801696929   11.27   27.30   \n",
       "189    7.9124245657167|6.3006664566035|29.545876041078    4.39    5.16   \n",
       "201     16.82410722784|16.417593211101|33.638471228762    5.16   13.57   \n",
       "21   85.639299887088|80.573726301079|49.93701185737...   60.39   15.31   \n",
       "204    49.111807279666|59.255590862254|84.429866025465    5.16   51.91   \n",
       "168    83.556394382622|87.168607186708|84.429866025465    7.94   83.75   \n",
       "93     31.933157258574|42.763116796961|41.431136806637   18.72   23.70   \n",
       "190     17.587488843199|16.61344109275|17.330497857902    4.39   13.57   \n",
       "9    84.437560776083|79.147453993463|45.47917675608...    8.52   60.39   \n",
       "154     21.561428077921|27.80961893085|17.330497857902    9.65   15.34   \n",
       "10   26.525123033003|30.246980924477|18.55069143247...    8.52   15.31   \n",
       "118     25.424594647239|17.324609254191|15.81938292618    8.05   11.92   \n",
       "96      76.194577460009|69.96379868456|70.908498877283   18.72   64.17   \n",
       "92      72.36583511067|65.156977174466|66.577589745711   44.72   40.53   \n",
       "82     29.013635434281|32.776745881878|28.763900501323    7.05   23.70   \n",
       "81     19.764958464342|29.047408996078|26.768210654182    7.05   18.72   \n",
       "56   85.778472921827|56.047851758911|68.31811701082...   61.28   41.86   \n",
       "20   94.063760696756|92.346083916258|86.62537041857...   75.63   51.07   \n",
       "132      84.37874531346|93.928045139959|95.05149545977   36.67   85.69   \n",
       "\n",
       "     kill_C  improve  improve_p  kill_entropy  sum_kill_dif  \\\n",
       "60    73.27    19.78       0.37          0.94        108.15   \n",
       "24    90.94    11.11       0.14          0.94         98.02   \n",
       "117   41.96     5.29       0.14          0.91         79.99   \n",
       "129   45.07     8.40       0.23          0.91         81.59   \n",
       "128   68.85    13.02       0.23          0.91         72.51   \n",
       "46    28.70    12.08       0.73          0.85         27.16   \n",
       "164   49.21    16.20       0.49          0.64         34.81   \n",
       "165   22.18     6.84       0.45          0.64         35.83   \n",
       "153   16.94     7.29       0.76          0.64         29.82   \n",
       "57    55.05     8.33       0.18          0.44        128.78   \n",
       "45    53.85     7.14       0.15          0.44        145.97   \n",
       "200   35.17     7.87       0.29          0.40         43.51   \n",
       "189    9.29     4.13       0.80          0.40         19.59   \n",
       "201   18.06     4.49       0.33          0.40         33.83   \n",
       "21    65.61     5.22       0.09          0.00        191.99   \n",
       "204   53.94     2.03       0.04          0.00        152.99   \n",
       "168   85.20     1.44       0.02          0.00        214.47   \n",
       "93    37.83    14.13       0.60          0.00         16.31   \n",
       "190   17.37     3.80       0.28          0.00         25.13   \n",
       "9     63.04     2.65       0.04          0.00        220.52   \n",
       "154   23.46     8.12       0.53          0.00         16.90   \n",
       "10    22.39     7.08       0.46          0.00         28.53   \n",
       "118   18.96     7.04       0.59          0.00         13.25   \n",
       "96    70.84     6.67       0.10          0.00        139.76   \n",
       "92    66.66    21.94       0.49          0.00         11.74   \n",
       "82    29.04     5.34       0.23          0.00         50.42   \n",
       "81    24.46     5.74       0.31          0.00         34.11   \n",
       "56    76.13    14.86       0.24          0.00        102.57   \n",
       "20    86.12    10.49       0.14          0.00        104.46   \n",
       "132   90.32     4.63       0.05          0.00        168.51   \n",
       "\n",
       "               Combi Name 1            Combi Name 2              Combi Name  \n",
       "60      Epothilone B|PI-103     PI-103|Epothilone B     Epothilone B|PI-103  \n",
       "24      Epothilone B|PI-103     PI-103|Epothilone B     Epothilone B|PI-103  \n",
       "117  Docetaxel|Epothilone B  Epothilone B|Docetaxel  Docetaxel|Epothilone B  \n",
       "129  Epothilone B|Gefitinib  Gefitinib|Epothilone B  Gefitinib|Epothilone B  \n",
       "128  Doxorubicin|Vorinostat  Vorinostat|Doxorubicin  Doxorubicin|Vorinostat  \n",
       "46      Docetaxel|Gefitinib     Gefitinib|Docetaxel     Docetaxel|Gefitinib  \n",
       "164  Doxorubicin|Vorinostat  Vorinostat|Doxorubicin  Doxorubicin|Vorinostat  \n",
       "165  Epothilone B|Gefitinib  Gefitinib|Epothilone B  Gefitinib|Epothilone B  \n",
       "153  Docetaxel|Epothilone B  Epothilone B|Docetaxel  Docetaxel|Epothilone B  \n",
       "57   Epothilone B|Gefitinib  Gefitinib|Epothilone B  Gefitinib|Epothilone B  \n",
       "45   Docetaxel|Epothilone B  Epothilone B|Docetaxel  Docetaxel|Epothilone B  \n",
       "200  Doxorubicin|Vorinostat  Vorinostat|Doxorubicin  Doxorubicin|Vorinostat  \n",
       "189  Docetaxel|Epothilone B  Epothilone B|Docetaxel  Docetaxel|Epothilone B  \n",
       "201  Epothilone B|Gefitinib  Gefitinib|Epothilone B  Gefitinib|Epothilone B  \n",
       "21   Epothilone B|Gefitinib  Gefitinib|Epothilone B  Gefitinib|Epothilone B  \n",
       "204     Epothilone B|PI-103     PI-103|Epothilone B     Epothilone B|PI-103  \n",
       "168     Epothilone B|PI-103     PI-103|Epothilone B     Epothilone B|PI-103  \n",
       "93   Epothilone B|Gefitinib  Gefitinib|Epothilone B  Gefitinib|Epothilone B  \n",
       "190     Docetaxel|Gefitinib     Gefitinib|Docetaxel     Docetaxel|Gefitinib  \n",
       "9    Docetaxel|Epothilone B  Epothilone B|Docetaxel  Docetaxel|Epothilone B  \n",
       "154     Docetaxel|Gefitinib     Gefitinib|Docetaxel     Docetaxel|Gefitinib  \n",
       "10      Docetaxel|Gefitinib     Gefitinib|Docetaxel     Docetaxel|Gefitinib  \n",
       "118     Docetaxel|Gefitinib     Gefitinib|Docetaxel     Docetaxel|Gefitinib  \n",
       "96      Epothilone B|PI-103     PI-103|Epothilone B     Epothilone B|PI-103  \n",
       "92   Doxorubicin|Vorinostat  Vorinostat|Doxorubicin  Doxorubicin|Vorinostat  \n",
       "82      Docetaxel|Gefitinib     Gefitinib|Docetaxel     Docetaxel|Gefitinib  \n",
       "81   Docetaxel|Epothilone B  Epothilone B|Docetaxel  Docetaxel|Epothilone B  \n",
       "56   Doxorubicin|Vorinostat  Vorinostat|Doxorubicin  Doxorubicin|Vorinostat  \n",
       "20   Doxorubicin|Vorinostat  Vorinostat|Doxorubicin  Doxorubicin|Vorinostat  \n",
       "132     Epothilone B|PI-103     PI-103|Epothilone B     Epothilone B|PI-103  "
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_combi_df.sort_values('kill_entropy', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:12.625354Z",
     "start_time": "2020-11-19T01:24:12.604045Z"
    }
   },
   "outputs": [],
   "source": [
    "pred_single_kill_df = pred_single_df[['patient', 'drug_name', 'kill']].pivot(index='drug_name', columns='patient', values='kill')\n",
    "pred_single_delta_df = pred_single_df[['patient', 'drug_name', 'delta']].pivot(index='drug_name', columns='patient', values='delta')\n",
    "pred_combi_kill_df = pred_combi_df[['patient', 'Combi Name', 'kill_C']].pivot(index='Combi Name', columns='patient', values='kill_C')\n",
    "\n",
    "obs_single_kill_df = obs_kill_df.loc[single_drug_list, patient_list]\n",
    "obs_combi_kill_df = obs_kill_df.loc[combi_drug_list, patient_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:12.637091Z",
     "start_time": "2020-11-19T01:24:12.628352Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set(font_scale=1.25)\n",
    "sns.set_style('ticks')\n",
    "\n",
    "cmap = plt.cm.get_cmap('tab10', 10)\n",
    "colors = cmap(np.linspace(0, 1, 10))\n",
    "patient_color_dict = dict(zip(patient_list, colors[0:len(patient_list)]))\n",
    "drug_color_dict = dict(zip(single_drug_list, colors[0:len(single_drug_list)]))\n",
    "combi_color_dict = dict(zip(combi_drug_list, colors[0:len(combi_drug_list)]))\n",
    "\n",
    "drug_marker_dict = dict(zip(single_drug_list, ['o', 'v', '^', '<', '>', 'd', 's']))\n",
    "combi_marker_dict = dict(zip(combi_drug_list, ['p', '*', 'P', 'X', 'h']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For single drug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:12.671926Z",
     "start_time": "2020-11-19T01:24:12.640315Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(125, 5)\n",
      "(125, 3)\n",
      "(125, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug</th>\n",
       "      <th>File name</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Patient</th>\n",
       "      <th>Observed % cell death</th>\n",
       "      <th>Predicted % cell death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN120</td>\n",
       "      <td>72.39</td>\n",
       "      <td>79.82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN137</td>\n",
       "      <td>77.52</td>\n",
       "      <td>53.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN148</td>\n",
       "      <td>81.74</td>\n",
       "      <td>64.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN159</td>\n",
       "      <td>84.66</td>\n",
       "      <td>85.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN160</td>\n",
       "      <td>51.99</td>\n",
       "      <td>83.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Drug                              File name Replicate Patient  \\\n",
       "120  PI-103  validation_replicates_2019_06_24.xlsx         2   HN120   \n",
       "121  PI-103  validation_replicates_2019_06_24.xlsx         2   HN137   \n",
       "122  PI-103  validation_replicates_2019_06_24.xlsx         2   HN148   \n",
       "123  PI-103  validation_replicates_2019_06_24.xlsx         2   HN159   \n",
       "124  PI-103  validation_replicates_2019_06_24.xlsx         2   HN160   \n",
       "\n",
       "     Observed % cell death  Predicted % cell death  \n",
       "120                  72.39                   79.82  \n",
       "121                  77.52                   53.50  \n",
       "122                  81.74                   64.17  \n",
       "123                  84.66                   85.69  \n",
       "124                  51.99                   83.75  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_df = obs_single_kill_df.loc[single_drug_list, patient_list].stack().reset_index()\n",
    "obs_df.columns = ['Drug', 'File name', 'Replicate', 'Patient', 'Observed % cell death']\n",
    "print (obs_df.shape)\n",
    "\n",
    "pred_df = pred_single_kill_df.loc[[d[0] for d in obs_single_kill_df.index], patient_list].stack().reset_index()\n",
    "pred_df.columns = ['Drug', 'Patient', 'Predicted % cell death']\n",
    "print (pred_df.shape)\n",
    "\n",
    "scatter_single_df = pd.concat([obs_df, pred_df[['Predicted % cell death']]], axis=1)\n",
    "scatter_single_df.loc[:, 'Replicate'] = scatter_single_df['Replicate'].astype(str)\n",
    "print (scatter_single_df.shape)\n",
    "scatter_single_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:12.720188Z",
     "start_time": "2020-11-19T01:24:12.673673Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">Observed % cell death</th>\n",
       "      <th colspan=\"3\" halign=\"left\">Predicted % cell death</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>median</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug</th>\n",
       "      <th>Patient</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Docetaxel</th>\n",
       "      <th>HN120</th>\n",
       "      <td>15.94</td>\n",
       "      <td>31.06</td>\n",
       "      <td>19.31</td>\n",
       "      <td>8.52</td>\n",
       "      <td>8.52</td>\n",
       "      <td>8.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HN137</th>\n",
       "      <td>9.41</td>\n",
       "      <td>38.61</td>\n",
       "      <td>32.16</td>\n",
       "      <td>14.76</td>\n",
       "      <td>14.76</td>\n",
       "      <td>14.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HN148</th>\n",
       "      <td>15.77</td>\n",
       "      <td>31.33</td>\n",
       "      <td>22.87</td>\n",
       "      <td>7.05</td>\n",
       "      <td>7.05</td>\n",
       "      <td>7.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HN159</th>\n",
       "      <td>13.81</td>\n",
       "      <td>24.83</td>\n",
       "      <td>18.72</td>\n",
       "      <td>8.05</td>\n",
       "      <td>8.05</td>\n",
       "      <td>8.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HN160</th>\n",
       "      <td>1.91</td>\n",
       "      <td>14.27</td>\n",
       "      <td>12.00</td>\n",
       "      <td>9.65</td>\n",
       "      <td>9.65</td>\n",
       "      <td>9.65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Observed % cell death               Predicted % cell death  \\\n",
       "                                    min    max median                    min   \n",
       "Drug      Patient                                                              \n",
       "Docetaxel HN120                   15.94  31.06  19.31                   8.52   \n",
       "          HN137                    9.41  38.61  32.16                  14.76   \n",
       "          HN148                   15.77  31.33  22.87                   7.05   \n",
       "          HN159                   13.81  24.83  18.72                   8.05   \n",
       "          HN160                    1.91  14.27  12.00                   9.65   \n",
       "\n",
       "                                 \n",
       "                     max median  \n",
       "Drug      Patient                \n",
       "Docetaxel HN120     8.52   8.52  \n",
       "          HN137    14.76  14.76  \n",
       "          HN148     7.05   7.05  \n",
       "          HN159     8.05   8.05  \n",
       "          HN160     9.65   9.65  "
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scatter_single_df.groupby(['Drug', 'Patient']).agg(['min', 'max', 'median']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:12.725053Z",
     "start_time": "2020-11-19T01:24:12.722355Z"
    }
   },
   "outputs": [],
   "source": [
    "# scatter_single_df = scatter_single_df[scatter_single_df['Patient'].isin(['HN137'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:13.212129Z",
     "start_time": "2020-11-19T01:24:12.730580Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Single drug | Pearson r = 0.66 (3.59e-06)\n",
      "Single drug [R-sq 33.06%]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5, style='ticks')\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "sns.scatterplot(data=scatter_single_df.groupby(['Drug', 'Patient']).mean().reset_index(), x='Observed % cell death', y='Predicted % cell death', hue='Patient', style='Drug', s=150, alpha=0.9, ax=ax)\n",
    "\n",
    "for _, row in scatter_single_df.groupby(['Drug', 'Patient']).agg(['min', 'max', 'median']).iterrows():\n",
    "    ax.plot([row[('Observed % cell death', 'min')], row[('Observed % cell death', 'max')]], \n",
    "            [row[('Predicted % cell death', 'median')], row[('Predicted % cell death', 'median')]], \n",
    "            color='grey', zorder=0, alpha=0.5)\n",
    "    \n",
    "\n",
    "vmin = scatter_single_df[['Observed % cell death', 'Predicted % cell death']].min().min()\n",
    "vmax = scatter_single_df[['Observed % cell death', 'Predicted % cell death']].max().max()\n",
    "\n",
    "ax.plot([vmin-5, vmax+5], [vmin-5, vmax+5], ls=\"--\", c=\".3\", zorder=0)\n",
    "ax.set_xlim((vmin-5, 100))\n",
    "ax.set_ylim((vmin-5, 100))\n",
    "\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), framealpha=0, markerscale=2)\n",
    "\n",
    "x = scatter_single_df.groupby(['Drug', 'Patient']).mean().reset_index()['Observed % cell death'].values\n",
    "y = scatter_single_df.groupby(['Drug', 'Patient']).mean().reset_index()['Predicted % cell death'].values\n",
    "\n",
    "scor, pval = stats.pearsonr(x, y)\n",
    "print ('Single drug | Pearson r = {:.2f} ({:.2e})'.format(scor, pval))\n",
    "\n",
    "r2 = metrics.r2_score(x, y)\n",
    "print ('Single drug [R-sq {:.2f}%]'.format(r2*100))\n",
    "\n",
    "if dosage_used == 'Median IC50':\n",
    "    ax.set_xlabel('Observed % cell death\\n(at drug-specific dosage)')\n",
    "elif dosage_used == '3 fold':\n",
    "    ax.set_xlabel('Observed % cell death\\n(at 3-fold dilution)')\n",
    "else:\n",
    "    ax.set_xlabel('Observed % cell death\\n({})'.format(dosage_used))\n",
    "\n",
    "sns.despine()\n",
    "\n",
    "# fig.savefig('../figure/Fig4E_single_drug_{}.svg'.format(dosage_used))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:13.218426Z",
     "start_time": "2020-11-19T01:24:13.215367Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# drug_list = sorted(list(set(scatter_df['Drug'])))\n",
    "# for drug in drug_list:\n",
    "#     d_y = scatter_df[scatter_df['Drug']==drug]['Observed % cell death'].values\n",
    "#     d_y_hat = scatter_df[scatter_df['Drug']==drug]['Predicted % cell death'].values\n",
    "#     print (\"{} [R-sq = {:.2f}%]\".format(drug, metrics.r2_score(d_y, d_y_hat)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:13.224602Z",
     "start_time": "2020-11-19T01:24:13.219970Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "# patient_list = sorted(list(set(scatter_df['Patient'])))\n",
    "# for patient in patient_list:\n",
    "#     d_y = scatter_df[scatter_df['Patient']==patient]['Observed % cell death'].values\n",
    "#     d_y_hat = scatter_df[scatter_df['Patient']==patient]['Predicted % cell death'].values\n",
    "#     print (\"{} [R-sq = {:.2f}%]\".format(patient, metrics.r2_score(d_y, d_y_hat)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For combi drug"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:13.253335Z",
     "start_time": "2020-11-19T01:24:13.226265Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(70, 5)\n",
      "(70, 3)\n",
      "(70, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug combination</th>\n",
       "      <th>File name</th>\n",
       "      <th>Replicate</th>\n",
       "      <th>Patient</th>\n",
       "      <th>Observed % cell death</th>\n",
       "      <th>Predicted % cell death</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN120</td>\n",
       "      <td>82.03</td>\n",
       "      <td>90.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN137</td>\n",
       "      <td>90.08</td>\n",
       "      <td>73.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN148</td>\n",
       "      <td>85.65</td>\n",
       "      <td>70.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN159</td>\n",
       "      <td>88.91</td>\n",
       "      <td>90.32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>validation_replicates_2019_06_24.xlsx</td>\n",
       "      <td>2</td>\n",
       "      <td>HN160</td>\n",
       "      <td>55.05</td>\n",
       "      <td>85.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Drug combination                              File name Replicate  \\\n",
       "65  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "66  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "67  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "68  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "69  Epothilone B|PI-103  validation_replicates_2019_06_24.xlsx         2   \n",
       "\n",
       "   Patient  Observed % cell death  Predicted % cell death  \n",
       "65   HN120                  82.03                   90.94  \n",
       "66   HN137                  90.08                   73.27  \n",
       "67   HN148                  85.65                   70.84  \n",
       "68   HN159                  88.91                   90.32  \n",
       "69   HN160                  55.05                   85.20  "
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_df = obs_combi_kill_df.loc[combi_drug_list, patient_list].stack().reset_index()\n",
    "obs_df.columns = ['Drug combination', 'File name', 'Replicate', 'Patient', 'Observed % cell death']\n",
    "print (obs_df.shape)\n",
    "\n",
    "pred_df = pred_combi_kill_df.loc[[d[0] for d in obs_combi_kill_df.index], patient_list].stack().reset_index()\n",
    "pred_df.columns = ['Drug combination', 'Patient', 'Predicted % cell death']\n",
    "print (pred_df.shape)\n",
    "\n",
    "scatter_combi_df = pd.concat([obs_df, pred_df[['Predicted % cell death']]], axis=1)\n",
    "scatter_combi_df.loc[:, 'Replicate'] = scatter_combi_df['Replicate'].astype(str)\n",
    "print (scatter_combi_df.shape)\n",
    "scatter_combi_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:13.652080Z",
     "start_time": "2020-11-19T01:24:13.255446Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Drug combination | 0.58 (2.30e-03)\n",
      "Drug combination [R-sq 7.48%]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.5, style='ticks')\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "sns.scatterplot(data=scatter_combi_df.groupby(['Drug combination', 'Patient']).mean().reset_index(), x='Observed % cell death', y='Predicted % cell death', hue='Drug combination', style='Patient', s=150, alpha=1.0)\n",
    "\n",
    "for _, row in scatter_combi_df.groupby(['Drug combination', 'Patient']).agg(['min', 'max', 'median']).iterrows():\n",
    "    ax.plot([row[('Observed % cell death', 'min')], row[('Observed % cell death', 'max')]], \n",
    "            [row[('Predicted % cell death', 'median')], row[('Predicted % cell death', 'median')]], \n",
    "            color='grey', zorder=0, alpha=0.5)\n",
    "\n",
    "vmin = scatter_combi_df[['Observed % cell death', 'Predicted % cell death']].min().min()\n",
    "vmax = scatter_combi_df[['Observed % cell death', 'Predicted % cell death']].max().max()\n",
    "\n",
    "ax.plot([vmin-5, vmax+5], [vmin-5, vmax+5], ls=\"--\", c=\".3\", zorder=0)\n",
    "ax.set_xlim((vmin-5, 100))\n",
    "ax.set_ylim((vmin-5, 100))\n",
    "\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), framealpha=0, markerscale=2)\n",
    "\n",
    "x = scatter_combi_df.groupby(['Drug combination', 'Patient']).mean().reset_index()['Observed % cell death'].values\n",
    "y = scatter_combi_df.groupby(['Drug combination', 'Patient']).mean().reset_index()['Predicted % cell death'].values\n",
    "\n",
    "scor, pval = stats.pearsonr(x, y)\n",
    "print ('Drug combination | {:.2f} ({:.2e})'.format(scor, pval))\n",
    "\n",
    "r2 = metrics.r2_score(x, y)\n",
    "print ('Drug combination [R-sq {:.2f}%]'.format(r2*100))\n",
    "\n",
    "if dosage_used == 'Median IC50':\n",
    "    ax.set_xlabel('Observed % cell death\\n(at drug-specific dosage)')\n",
    "if dosage_used == '3 fold':\n",
    "    ax.set_xlabel('Observed % cell death\\n(at 3-fold dilution)')\n",
    "else:\n",
    "    ax.set_xlabel('Observed % cell death\\n({})'.format(dosage_used))\n",
    "\n",
    "sns.despine()\n",
    "\n",
    "# fig.savefig('../figure/Fig4_combi_drug_{}.svg'.format(dosage_used))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Barchart for single vs combi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:13.656631Z",
     "start_time": "2020-11-19T01:24:13.653947Z"
    }
   },
   "outputs": [],
   "source": [
    "# selected_patient = 'HN137' # HN137, HN120, HN148, HN159, HN160"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:13.662281Z",
     "start_time": "2020-11-19T01:24:13.658758Z"
    }
   },
   "outputs": [],
   "source": [
    "# sorted_combi_list = obs_bar_df[obs_bar_df['drug_name']=='C'].groupby('combi_name').mean().sort_values('kill').index\n",
    "# sorted_combi_list = ['Docetaxel\\nGefitinib', 'Doxorubicin\\nVorinostat',\n",
    "#        'Docetaxel\\nEpothilone B', 'Gefitinib\\nEpothilone B',\n",
    "#        'Epothilone B\\nPI-103']\n",
    "sorted_combi_list = ['Docetaxel\\nEpothilone B', 'Docetaxel\\nGefitinib', 'Doxorubicin\\nVorinostat', \n",
    "                     'Epothilone B\\nPI-103', 'Gefitinib\\nEpothilone B']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:14.715438Z",
     "start_time": "2020-11-19T01:24:13.664255Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1560x720 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.25, style='ticks')\n",
    "fig, axes = plt.subplots(figsize=(13, 6), nrows=1, ncols=5, sharey=True, sharex=True)\n",
    "\n",
    "\n",
    "for selected_patient, ax in zip(['HN120', 'HN137', 'HN148', 'HN159', 'HN160'], axes.flatten()):\n",
    "\n",
    "    obs_bar_list = []\n",
    "\n",
    "    for _, row in scatter_combi_df[scatter_combi_df['Patient']==selected_patient].iterrows():\n",
    "        combi_name = row['Drug combination']\n",
    "        combi_kills = row['Observed % cell death']\n",
    "\n",
    "        drug_a, drug_b = combi_name.split('|')\n",
    "        combi_name = '{}\\n{}'.format(drug_a, drug_b)\n",
    "\n",
    "        drug_a_kills = scatter_single_df[(scatter_single_df['Patient']==selected_patient) & (scatter_single_df['Drug']==drug_a)]['Observed % cell death'].values\n",
    "        drug_b_kills = scatter_single_df[(scatter_single_df['Patient']==selected_patient) & (scatter_single_df['Drug']==drug_b)]['Observed % cell death'].values\n",
    "\n",
    "        for drug_a_kill in drug_a_kills:\n",
    "            obs_bar_list += [['A', drug_a_kill, combi_name]]\n",
    "        for drug_b_kill in drug_b_kills:\n",
    "            obs_bar_list += [['B', drug_b_kill, combi_name]]\n",
    "\n",
    "        if type(row['Observed % cell death']) is float:\n",
    "            obs_bar_list += [['C', combi_kills, combi_name]]\n",
    "        else:\n",
    "            for combi_kill in combi_kills:\n",
    "                obs_bar_list += [['C', combi_kill, combi_name]]\n",
    "\n",
    "    obs_bar_df = pd.DataFrame(obs_bar_list, columns=['drug_name', 'kill', 'combi_name'])\n",
    "    clrs = ['orange' if d == 'C' else 'grey' for d in obs_bar_df.groupby(['combi_name', 'drug_name']).median().reset_index()['drug_name'].values]\n",
    "\n",
    "    sns.barplot(data=obs_bar_df, y='combi_name', x='kill', hue='drug_name', order=sorted_combi_list, palette=clrs, \n",
    "                estimator=np.median, ci='sd', ax=ax)\n",
    "    ax.set_xlim((0, 100))\n",
    "\n",
    "    ax.get_legend().remove()\n",
    "    ax.set_ylabel('')\n",
    "    ax.set_yticks([])\n",
    "    \n",
    "    ax.set_title(selected_patient, fontsize=16)\n",
    "    \n",
    "#     if dosage_used == '3 fold':\n",
    "#         ax.set_xlabel('Observed % cell death\\n(3-fold dilution)')\n",
    "#     else:\n",
    "#         ax.set_xlabel('Observed % cell death\\n({})'.format(dosage_used))\n",
    "    ax.set_xlabel('Observed % cell death')\n",
    "        \n",
    "plt.tight_layout()\n",
    "sns.despine()\n",
    "\n",
    "# fig.savefig('../figure/Fig4_single_combi_{}_all_obs.svg'.format(dosage_used))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:15.571319Z",
     "start_time": "2020-11-19T01:24:14.717889Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1560x720 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.25, style='ticks')\n",
    "fig, axes = plt.subplots(figsize=(13, 6), nrows=1, ncols=5, sharey=True)\n",
    "\n",
    "clrs = ['orange' if d == 'C' else 'grey' for d in obs_bar_df.groupby(['combi_name', 'drug_name']).mean().reset_index()['drug_name'].values]\n",
    "\n",
    "\n",
    "for selected_patient, ax in zip(['HN120', 'HN137', 'HN148', 'HN159', 'HN160'], axes.flatten()):\n",
    "\n",
    "    pred_bar_list = []\n",
    "\n",
    "    for _, row in scatter_combi_df[scatter_combi_df['Patient']==selected_patient].iterrows():\n",
    "        combi_name = row['Drug combination']\n",
    "        combi_kill = row['Predicted % cell death']\n",
    "\n",
    "        drug_a, drug_b = combi_name.split('|')\n",
    "        combi_name = '{}\\n{}'.format(drug_a, drug_b)\n",
    "\n",
    "        drug_a_kill = scatter_single_df[(scatter_single_df['Patient']==selected_patient) & (scatter_single_df['Drug']==drug_a)]['Predicted % cell death'].values.mean()\n",
    "        drug_b_kill = scatter_single_df[(scatter_single_df['Patient']==selected_patient) & (scatter_single_df['Drug']==drug_b)]['Predicted % cell death'].values.mean()\n",
    "\n",
    "        pred_bar_list += [['A', drug_a_kill, combi_name]]\n",
    "        pred_bar_list += [['B', drug_b_kill, combi_name]]\n",
    "        pred_bar_list += [['C', combi_kill, combi_name]]\n",
    "\n",
    "    pred_bar_df = pd.DataFrame(pred_bar_list, columns=['drug_name', 'kill', 'combi_name'])\n",
    "\n",
    "#     sns.set(font_scale=1.25, style='ticks')\n",
    "#     fig, ax = plt.subplots(figsize=(7, 3))\n",
    "\n",
    "#     clrs = ['orange' if d == 'C' else 'grey' for d in pred_bar_df['drug_name'].values]\n",
    "#     sns.barplot(data=pred_bar_df, x='combi_name', y='kill', hue='drug_name', order=sorted_combi_list, palette=clrs, ci=None, ax=ax)\n",
    "    \n",
    "    sns.barplot(data=pred_bar_df, y='combi_name', x='kill', hue='drug_name', order=sorted_combi_list, palette=clrs, \n",
    "                estimator=np.median, ci=None, ax=ax)\n",
    "    ax.set_xlim((0, 100))\n",
    "\n",
    "    ax.get_legend().remove()\n",
    "    ax.set_ylabel('')\n",
    "    ax.set_yticks([])\n",
    "    ax.set_xlabel('\\nPredicted % cell death')\n",
    "\n",
    "    ax.set_title(selected_patient, fontsize=16)\n",
    "    \n",
    "plt.tight_layout()\n",
    "sns.despine()\n",
    "\n",
    "# fig.savefig('../figure/Fig4_single_combi_all_pred.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:15.581581Z",
     "start_time": "2020-11-19T01:24:15.572943Z"
    }
   },
   "outputs": [
    {
     "ename": "AssertionError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-147-a871fdc9ebee>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAssertionError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "assert False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare improvement of drug combi over single drugs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:21.848647Z",
     "start_time": "2020-11-19T01:24:21.835487Z"
    }
   },
   "outputs": [],
   "source": [
    "pred_combi_imp_df = pred_combi_df[['patient', 'Combi Name', 'improve']].pivot(index='Combi Name', columns='patient', values='improve')\n",
    "pred_combi_pimp_df = pred_combi_df[['patient', 'Combi Name', 'improve_p']].pivot(index='Combi Name', columns='patient', values='improve_p')\n",
    "\n",
    "pred_combi_imp_df = pred_combi_imp_df.loc[combi_drug_list, patient_list]\n",
    "pred_combi_pimp_df = pred_combi_pimp_df.loc[combi_drug_list, patient_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:21.986607Z",
     "start_time": "2020-11-19T01:24:21.977426Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>patient</th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Combi Name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Docetaxel|Epothilone B</th>\n",
       "      <td>2.65</td>\n",
       "      <td>7.14</td>\n",
       "      <td>5.74</td>\n",
       "      <td>5.29</td>\n",
       "      <td>7.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Docetaxel|Gefitinib</th>\n",
       "      <td>7.08</td>\n",
       "      <td>12.08</td>\n",
       "      <td>5.34</td>\n",
       "      <td>7.04</td>\n",
       "      <td>8.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gefitinib|Epothilone B</th>\n",
       "      <td>5.22</td>\n",
       "      <td>8.33</td>\n",
       "      <td>14.13</td>\n",
       "      <td>8.40</td>\n",
       "      <td>6.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epothilone B|PI-103</th>\n",
       "      <td>11.11</td>\n",
       "      <td>19.78</td>\n",
       "      <td>6.67</td>\n",
       "      <td>4.63</td>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Doxorubicin|Vorinostat</th>\n",
       "      <td>10.49</td>\n",
       "      <td>14.86</td>\n",
       "      <td>21.94</td>\n",
       "      <td>13.02</td>\n",
       "      <td>16.20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "patient                 HN120  HN137  HN148  HN159  HN160\n",
       "Combi Name                                               \n",
       "Docetaxel|Epothilone B   2.65   7.14   5.74   5.29   7.29\n",
       "Docetaxel|Gefitinib      7.08  12.08   5.34   7.04   8.12\n",
       "Gefitinib|Epothilone B   5.22   8.33  14.13   8.40   6.84\n",
       "Epothilone B|PI-103     11.11  19.78   6.67   4.63   1.44\n",
       "Doxorubicin|Vorinostat  10.49  14.86  21.94  13.02  16.20"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_combi_imp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:22.130299Z",
     "start_time": "2020-11-19T01:24:22.110926Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "      <th>HN182</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Docetaxel|Epothilone B</th>\n",
       "      <td>56.18</td>\n",
       "      <td>64.47</td>\n",
       "      <td>40.28</td>\n",
       "      <td>44.71</td>\n",
       "      <td>23.88</td>\n",
       "      <td>60.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Docetaxel|Gefitinib</th>\n",
       "      <td>44.57</td>\n",
       "      <td>46.33</td>\n",
       "      <td>56.31</td>\n",
       "      <td>42.29</td>\n",
       "      <td>7.71</td>\n",
       "      <td>38.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Doxorubicin|Vorinostat</th>\n",
       "      <td>45.67</td>\n",
       "      <td>54.62</td>\n",
       "      <td>41.34</td>\n",
       "      <td>24.28</td>\n",
       "      <td>13.93</td>\n",
       "      <td>56.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epothilone B|PI-103</th>\n",
       "      <td>82.10</td>\n",
       "      <td>90.42</td>\n",
       "      <td>86.49</td>\n",
       "      <td>89.11</td>\n",
       "      <td>56.33</td>\n",
       "      <td>40.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gefitinib|Epothilone B</th>\n",
       "      <td>67.19</td>\n",
       "      <td>70.42</td>\n",
       "      <td>64.15</td>\n",
       "      <td>62.17</td>\n",
       "      <td>22.70</td>\n",
       "      <td>65.37</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        HN120  HN137  HN148  HN159  HN160  HN182\n",
       "Drug                                                            \n",
       "Docetaxel|Epothilone B  56.18  64.47  40.28  44.71  23.88  60.23\n",
       "Docetaxel|Gefitinib     44.57  46.33  56.31  42.29   7.71  38.34\n",
       "Doxorubicin|Vorinostat  45.67  54.62  41.34  24.28  13.93  56.48\n",
       "Epothilone B|PI-103     82.10  90.42  86.49  89.11  56.33  40.31\n",
       "Gefitinib|Epothilone B  67.19  70.42  64.15  62.17  22.70  65.37"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TEMPORARY: using averge response\n",
    "\n",
    "temp_df = obs_kill_df.loc[combi_drug_list].reset_index().drop(['File name', 'Replicate'], axis=1)\n",
    "temp_df = temp_df.groupby('Drug').median()\n",
    "# temp_df = temp_df.loc[~temp_df.index.duplicated(keep='first')]\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:22.375295Z",
     "start_time": "2020-11-19T01:24:22.243961Z"
    }
   },
   "outputs": [],
   "source": [
    "imp_results = []\n",
    "pimp_results = []\n",
    "for c, data in temp_df.loc[combi_drug_list, patient_list].iterrows():\n",
    "    a, b = c.split('|')\n",
    "    \n",
    "    c_kill = data.values\n",
    "#     best_kill = obs_kill_df.loc[[a, b]].max()[patient_list]\n",
    "    best_kill = obs_kill_df.loc[[a, b]].reset_index().groupby('Drug').median()[patient_list].max()\n",
    "    \n",
    "#     print (c, c_kill, best_kill)\n",
    "    \n",
    "    imp = list((c_kill - best_kill).values)\n",
    "    pimp = list(((c_kill - best_kill)/best_kill).values)\n",
    "    \n",
    "    imp_results += [[c] + imp]\n",
    "    pimp_results += [[c] + pimp]\n",
    "        \n",
    "obs_combi_imp_df = pd.DataFrame(imp_results)\n",
    "obs_combi_imp_df.columns = ['Drug combination'] + patient_list\n",
    "obs_combi_imp_df = obs_combi_imp_df.set_index('Drug combination')\n",
    "\n",
    "obs_combi_pimp_df = pd.DataFrame(pimp_results)\n",
    "obs_combi_pimp_df.columns = ['Drug combination'] + patient_list\n",
    "obs_combi_pimp_df = obs_combi_pimp_df.set_index('Drug combination')\n",
    "\n",
    "obs_combi_imp_df = obs_combi_imp_df.loc[combi_drug_list, patient_list]\n",
    "obs_combi_pimp_df = obs_combi_pimp_df.loc[combi_drug_list, patient_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:22.399413Z",
     "start_time": "2020-11-19T01:24:22.386673Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "HN120    35.72\n",
       "HN137    46.45\n",
       "HN148    25.15\n",
       "HN159    22.23\n",
       "HN160    14.64\n",
       "dtype: float64"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_kill_df.loc[[a, b]].reset_index().groupby('Drug').median()[patient_list].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:22.527351Z",
     "start_time": "2020-11-19T01:24:22.518163Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HN120</th>\n",
       "      <th>HN137</th>\n",
       "      <th>HN148</th>\n",
       "      <th>HN159</th>\n",
       "      <th>HN160</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Drug combination</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Docetaxel|Epothilone B</th>\n",
       "      <td>3.50</td>\n",
       "      <td>1.67</td>\n",
       "      <td>3.17</td>\n",
       "      <td>2.27</td>\n",
       "      <td>4.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Docetaxel|Gefitinib</th>\n",
       "      <td>14.69</td>\n",
       "      <td>14.17</td>\n",
       "      <td>8.90</td>\n",
       "      <td>15.88</td>\n",
       "      <td>-4.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gefitinib|Epothilone B</th>\n",
       "      <td>14.52</td>\n",
       "      <td>7.62</td>\n",
       "      <td>16.74</td>\n",
       "      <td>19.74</td>\n",
       "      <td>3.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Epothilone B|PI-103</th>\n",
       "      <td>7.90</td>\n",
       "      <td>13.51</td>\n",
       "      <td>5.04</td>\n",
       "      <td>4.13</td>\n",
       "      <td>5.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Doxorubicin|Vorinostat</th>\n",
       "      <td>9.95</td>\n",
       "      <td>8.16</td>\n",
       "      <td>16.19</td>\n",
       "      <td>2.05</td>\n",
       "      <td>-0.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        HN120  HN137  HN148  HN159  HN160\n",
       "Drug combination                                         \n",
       "Docetaxel|Epothilone B   3.50   1.67   3.17   2.27   4.89\n",
       "Docetaxel|Gefitinib     14.69  14.17   8.90  15.88  -4.29\n",
       "Gefitinib|Epothilone B  14.52   7.62  16.74  19.74   3.72\n",
       "Epothilone B|PI-103      7.90  13.51   5.04   4.13   5.19\n",
       "Doxorubicin|Vorinostat   9.95   8.16  16.19   2.05  -0.71"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs_combi_imp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:23.040062Z",
     "start_time": "2020-11-19T01:24:22.651481Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "obs_df = obs_combi_imp_df.loc[combi_drug_list, patient_list].stack().reset_index()\n",
    "obs_df.columns = ['Drug combination', 'Patient', 'Observed % death increase']\n",
    "\n",
    "pred_df = pred_combi_imp_df.loc[combi_drug_list, patient_list].stack().reset_index()\n",
    "pred_df.columns = ['Drug combination', 'Patient', 'Predicted % death increase']\n",
    "\n",
    "sns.set(font_scale=1.25, style='ticks')\n",
    "fig, ax = plt.subplots(figsize=(8,6))\n",
    "scatter_df = pd.merge(obs_df, pred_df, left_on=['Drug combination', 'Patient'], right_on=['Drug combination', 'Patient'])\n",
    "\n",
    "sns.scatterplot(data=scatter_df, x='Observed % death increase', y='Predicted % death increase', hue='Drug combination', style='Patient', s=100, alpha=0.7)\n",
    "# sns.regplot(data=scatter_df, x='Observed % death increase', y='Predicted % death increase', x_ci='ci', ci=99, scatter=False, color='grey')\n",
    "# plt.plot([0, 25], [0, 25], ls=\"--\", c=\".3\")\n",
    "\n",
    "vmin = scatter_df[['Observed % death increase', 'Predicted % death increase']].min().min()\n",
    "vmax = scatter_df[['Observed % death increase', 'Predicted % death increase']].max().max()\n",
    "\n",
    "# ax.plot([vmin-5, vmax+5], [vmin-5, vmax+5], ls=\"--\", c=\".3\", zorder=0)\n",
    "# ax.set_xlim((vmin-5, vmax+5))\n",
    "# ax.set_ylim((vmin-5, vmax+5))\n",
    "\n",
    "box = ax.get_position()\n",
    "ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])\n",
    "ax.legend(loc='center left', bbox_to_anchor=(1, 0.5), framealpha=0)\n",
    "\n",
    "scor, pval = stats.pearsonr(obs_df['Observed % death increase'].values, pred_df['Predicted % death increase'].values)\n",
    "ax.set_title('Single VS Combi\\n(Pearson r = {:.2f} p-val < {:.2e})'.format(scor, pval))\n",
    "\n",
    "# r2 = metrics.r2_score(scatter_df['Observed % death increase'].values, scatter_df['Predicted % death increase'].values)\n",
    "# ax.set_title('Single VS Combi [R-sq {:.2f}%]'.format(r2*100))\n",
    "\n",
    "plt.tight_layout()\n",
    "# fig.savefig('../figure/Fig4_improvement_{}_scatter.svg'.format(dosage_used))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:23.059045Z",
     "start_time": "2020-11-19T01:24:23.043134Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug combination</th>\n",
       "      <th>Patient</th>\n",
       "      <th>Observed % death increase</th>\n",
       "      <th>Predicted % death increase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN148</td>\n",
       "      <td>16.19</td>\n",
       "      <td>21.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN137</td>\n",
       "      <td>13.51</td>\n",
       "      <td>19.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN160</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>16.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN137</td>\n",
       "      <td>8.16</td>\n",
       "      <td>14.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN148</td>\n",
       "      <td>16.74</td>\n",
       "      <td>14.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN159</td>\n",
       "      <td>2.05</td>\n",
       "      <td>13.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN137</td>\n",
       "      <td>14.17</td>\n",
       "      <td>12.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN120</td>\n",
       "      <td>7.90</td>\n",
       "      <td>11.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN120</td>\n",
       "      <td>9.95</td>\n",
       "      <td>10.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN159</td>\n",
       "      <td>19.74</td>\n",
       "      <td>8.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN137</td>\n",
       "      <td>7.62</td>\n",
       "      <td>8.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN160</td>\n",
       "      <td>-4.29</td>\n",
       "      <td>8.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN160</td>\n",
       "      <td>4.89</td>\n",
       "      <td>7.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN137</td>\n",
       "      <td>1.67</td>\n",
       "      <td>7.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN120</td>\n",
       "      <td>14.69</td>\n",
       "      <td>7.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN159</td>\n",
       "      <td>15.88</td>\n",
       "      <td>7.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN160</td>\n",
       "      <td>3.72</td>\n",
       "      <td>6.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN148</td>\n",
       "      <td>5.04</td>\n",
       "      <td>6.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN148</td>\n",
       "      <td>3.17</td>\n",
       "      <td>5.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN148</td>\n",
       "      <td>8.90</td>\n",
       "      <td>5.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN159</td>\n",
       "      <td>2.27</td>\n",
       "      <td>5.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN120</td>\n",
       "      <td>14.52</td>\n",
       "      <td>5.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN159</td>\n",
       "      <td>4.13</td>\n",
       "      <td>4.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN120</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN160</td>\n",
       "      <td>5.19</td>\n",
       "      <td>1.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Drug combination Patient  Observed % death increase  \\\n",
       "22  Doxorubicin|Vorinostat   HN148                      16.19   \n",
       "16     Epothilone B|PI-103   HN137                      13.51   \n",
       "24  Doxorubicin|Vorinostat   HN160                      -0.71   \n",
       "21  Doxorubicin|Vorinostat   HN137                       8.16   \n",
       "12  Gefitinib|Epothilone B   HN148                      16.74   \n",
       "23  Doxorubicin|Vorinostat   HN159                       2.05   \n",
       "6      Docetaxel|Gefitinib   HN137                      14.17   \n",
       "15     Epothilone B|PI-103   HN120                       7.90   \n",
       "20  Doxorubicin|Vorinostat   HN120                       9.95   \n",
       "13  Gefitinib|Epothilone B   HN159                      19.74   \n",
       "11  Gefitinib|Epothilone B   HN137                       7.62   \n",
       "9      Docetaxel|Gefitinib   HN160                      -4.29   \n",
       "4   Docetaxel|Epothilone B   HN160                       4.89   \n",
       "1   Docetaxel|Epothilone B   HN137                       1.67   \n",
       "5      Docetaxel|Gefitinib   HN120                      14.69   \n",
       "8      Docetaxel|Gefitinib   HN159                      15.88   \n",
       "14  Gefitinib|Epothilone B   HN160                       3.72   \n",
       "17     Epothilone B|PI-103   HN148                       5.04   \n",
       "2   Docetaxel|Epothilone B   HN148                       3.17   \n",
       "7      Docetaxel|Gefitinib   HN148                       8.90   \n",
       "3   Docetaxel|Epothilone B   HN159                       2.27   \n",
       "10  Gefitinib|Epothilone B   HN120                      14.52   \n",
       "18     Epothilone B|PI-103   HN159                       4.13   \n",
       "0   Docetaxel|Epothilone B   HN120                       3.50   \n",
       "19     Epothilone B|PI-103   HN160                       5.19   \n",
       "\n",
       "    Predicted % death increase  \n",
       "22                       21.94  \n",
       "16                       19.78  \n",
       "24                       16.20  \n",
       "21                       14.86  \n",
       "12                       14.13  \n",
       "23                       13.02  \n",
       "6                        12.08  \n",
       "15                       11.11  \n",
       "20                       10.49  \n",
       "13                        8.40  \n",
       "11                        8.33  \n",
       "9                         8.12  \n",
       "4                         7.29  \n",
       "1                         7.14  \n",
       "5                         7.08  \n",
       "8                         7.04  \n",
       "14                        6.84  \n",
       "17                        6.67  \n",
       "2                         5.74  \n",
       "7                         5.34  \n",
       "3                         5.29  \n",
       "10                        5.22  \n",
       "18                        4.63  \n",
       "0                         2.65  \n",
       "19                        1.44  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scatter_df.sort_values('Predicted % death increase', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:23.066095Z",
     "start_time": "2020-11-19T01:24:23.061273Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.617319186613976"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scatter_df['Observed % death increase'].median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:23.074044Z",
     "start_time": "2020-11-19T01:24:23.068315Z"
    },
    "code_folding": [
     0
    ]
   },
   "outputs": [],
   "source": [
    "def change_boxplot_edge_color(ax, col):\n",
    "    for i, artist in enumerate(ax.artists):\n",
    "        # Set the linecolor on the artist to the facecolor, and set the facecolor to None\n",
    "        artist.set_edgecolor(col)\n",
    "        # artist.set_facecolor('None')\n",
    "\n",
    "        # Each box has 6 associated Line2D objects (to make the whiskers, fliers, etc.)\n",
    "        # Loop over them here, and use the same colour as above\n",
    "        for j in range(i*6,i*6+6):\n",
    "            line = ax.lines[j]\n",
    "            line.set_color(col)\n",
    "            line.set_mfc(col)\n",
    "            line.set_mec(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:23.208514Z",
     "start_time": "2020-11-19T01:24:23.206132Z"
    }
   },
   "outputs": [],
   "source": [
    "cutoff = 7.6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:23.546201Z",
     "start_time": "2020-11-19T01:24:23.345245Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.121320343559643 3.39e-02\n",
      "2.179679669331107 3.98e-02\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(font_scale=1.1, style='ticks')\n",
    "fig, ax = plt.subplots(figsize=(3, 4))\n",
    "\n",
    "merge_df = pd.merge(obs_df, pred_df, left_on=['Drug combination', 'Patient'], right_on=['Drug combination', 'Patient'])\n",
    "merge_df.loc[:, 'Observed increase'] = merge_df['Observed % death increase'] >= cutoff\n",
    "\n",
    "sns.boxplot(data=merge_df, x='Observed increase', y='Predicted % death increase', fliersize=4, color='lightblue', ax=ax)\n",
    "# sns.swarmplot(data=merge_df, x='Observed increase', y='Predicted % death increase', color='black', alpha=0.5, ax=ax)\n",
    "change_boxplot_edge_color(ax, 'black')\n",
    "ax.set_xlabel('Observed increase ' + r'$\\geq {}\\%$'.format(cutoff))\n",
    "\n",
    "x = merge_df.loc[merge_df['Observed increase'], 'Predicted % death increase'].values\n",
    "y = merge_df.loc[~merge_df['Observed increase'], 'Predicted % death increase'].values\n",
    "\n",
    "sns.despine()\n",
    "print (\"{} {:.2e}\\n{} {:.2e}\".format(*stats.ranksums(x, y), *stats.ttest_ind(x, y)))\n",
    "plt.tight_layout()\n",
    "\n",
    "# fig.savefig('../figure/Fig4_improvement_{}_{}_median_cutoff.svg'.format(dosage_used, cutoff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:23.563308Z",
     "start_time": "2020-11-19T01:24:23.548002Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Drug combination</th>\n",
       "      <th>Patient</th>\n",
       "      <th>Observed % death increase</th>\n",
       "      <th>Predicted % death increase</th>\n",
       "      <th>Observed increase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN120</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2.65</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN137</td>\n",
       "      <td>1.67</td>\n",
       "      <td>7.14</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN148</td>\n",
       "      <td>3.17</td>\n",
       "      <td>5.74</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN159</td>\n",
       "      <td>2.27</td>\n",
       "      <td>5.29</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Docetaxel|Epothilone B</td>\n",
       "      <td>HN160</td>\n",
       "      <td>4.89</td>\n",
       "      <td>7.29</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN160</td>\n",
       "      <td>-4.29</td>\n",
       "      <td>8.12</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN159</td>\n",
       "      <td>2.05</td>\n",
       "      <td>13.02</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN160</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>16.20</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN148</td>\n",
       "      <td>5.04</td>\n",
       "      <td>6.67</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN159</td>\n",
       "      <td>4.13</td>\n",
       "      <td>4.63</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN160</td>\n",
       "      <td>5.19</td>\n",
       "      <td>1.44</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN160</td>\n",
       "      <td>3.72</td>\n",
       "      <td>6.84</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN120</td>\n",
       "      <td>14.69</td>\n",
       "      <td>7.08</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN137</td>\n",
       "      <td>14.17</td>\n",
       "      <td>12.08</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN148</td>\n",
       "      <td>8.90</td>\n",
       "      <td>5.34</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Docetaxel|Gefitinib</td>\n",
       "      <td>HN159</td>\n",
       "      <td>15.88</td>\n",
       "      <td>7.04</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN120</td>\n",
       "      <td>9.95</td>\n",
       "      <td>10.49</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN137</td>\n",
       "      <td>8.16</td>\n",
       "      <td>14.86</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Doxorubicin|Vorinostat</td>\n",
       "      <td>HN148</td>\n",
       "      <td>16.19</td>\n",
       "      <td>21.94</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN120</td>\n",
       "      <td>7.90</td>\n",
       "      <td>11.11</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Epothilone B|PI-103</td>\n",
       "      <td>HN137</td>\n",
       "      <td>13.51</td>\n",
       "      <td>19.78</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN120</td>\n",
       "      <td>14.52</td>\n",
       "      <td>5.22</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN137</td>\n",
       "      <td>7.62</td>\n",
       "      <td>8.33</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN148</td>\n",
       "      <td>16.74</td>\n",
       "      <td>14.13</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Gefitinib|Epothilone B</td>\n",
       "      <td>HN159</td>\n",
       "      <td>19.74</td>\n",
       "      <td>8.40</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Drug combination Patient  Observed % death increase  \\\n",
       "0   Docetaxel|Epothilone B   HN120                       3.50   \n",
       "1   Docetaxel|Epothilone B   HN137                       1.67   \n",
       "2   Docetaxel|Epothilone B   HN148                       3.17   \n",
       "3   Docetaxel|Epothilone B   HN159                       2.27   \n",
       "4   Docetaxel|Epothilone B   HN160                       4.89   \n",
       "9      Docetaxel|Gefitinib   HN160                      -4.29   \n",
       "23  Doxorubicin|Vorinostat   HN159                       2.05   \n",
       "24  Doxorubicin|Vorinostat   HN160                      -0.71   \n",
       "17     Epothilone B|PI-103   HN148                       5.04   \n",
       "18     Epothilone B|PI-103   HN159                       4.13   \n",
       "19     Epothilone B|PI-103   HN160                       5.19   \n",
       "14  Gefitinib|Epothilone B   HN160                       3.72   \n",
       "5      Docetaxel|Gefitinib   HN120                      14.69   \n",
       "6      Docetaxel|Gefitinib   HN137                      14.17   \n",
       "7      Docetaxel|Gefitinib   HN148                       8.90   \n",
       "8      Docetaxel|Gefitinib   HN159                      15.88   \n",
       "20  Doxorubicin|Vorinostat   HN120                       9.95   \n",
       "21  Doxorubicin|Vorinostat   HN137                       8.16   \n",
       "22  Doxorubicin|Vorinostat   HN148                      16.19   \n",
       "15     Epothilone B|PI-103   HN120                       7.90   \n",
       "16     Epothilone B|PI-103   HN137                      13.51   \n",
       "10  Gefitinib|Epothilone B   HN120                      14.52   \n",
       "11  Gefitinib|Epothilone B   HN137                       7.62   \n",
       "12  Gefitinib|Epothilone B   HN148                      16.74   \n",
       "13  Gefitinib|Epothilone B   HN159                      19.74   \n",
       "\n",
       "    Predicted % death increase  Observed increase  \n",
       "0                         2.65              False  \n",
       "1                         7.14              False  \n",
       "2                         5.74              False  \n",
       "3                         5.29              False  \n",
       "4                         7.29              False  \n",
       "9                         8.12              False  \n",
       "23                       13.02              False  \n",
       "24                       16.20              False  \n",
       "17                        6.67              False  \n",
       "18                        4.63              False  \n",
       "19                        1.44              False  \n",
       "14                        6.84              False  \n",
       "5                         7.08               True  \n",
       "6                        12.08               True  \n",
       "7                         5.34               True  \n",
       "8                         7.04               True  \n",
       "20                       10.49               True  \n",
       "21                       14.86               True  \n",
       "22                       21.94               True  \n",
       "15                       11.11               True  \n",
       "16                       19.78               True  \n",
       "10                        5.22               True  \n",
       "11                        8.33               True  \n",
       "12                       14.13               True  \n",
       "13                        8.40               True  "
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_df.sort_values(['Observed increase', 'Drug combination'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:23.646200Z",
     "start_time": "2020-11-19T01:24:23.639444Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.12 (3.39e-02)\n"
     ]
    }
   ],
   "source": [
    "print (\"{:.2f} ({:.2e})\".format(*stats.ranksums(x, y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:23.784244Z",
     "start_time": "2020-11-19T01:24:23.776513Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({0: 12, 1: 13})"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "obs = (merge_df['Observed % death increase'].values >= cutoff).astype(int)\n",
    "pred = (merge_df['Predicted % death increase'].values >= cutoff).astype(int)\n",
    "pred_val = merge_df['Predicted % death increase'].values\n",
    "\n",
    "Counter(obs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:23.939182Z",
     "start_time": "2020-11-19T01:24:23.931950Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.72, 0.7199999999999999, 0.7199999999999999)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(obs, pred), metrics.f1_score(obs, pred, pos_label=1), metrics.f1_score(obs, pred, pos_label=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-11-19T01:24:24.277294Z",
     "start_time": "2020-11-19T01:24:24.083967Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7515622170227434\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.0, 1.1)"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "precision, recall, thresholds = metrics.precision_recall_curve(obs, pred_val)\n",
    "plt.plot(recall, precision)\n",
    "\n",
    "print (metrics.auc(recall, precision))\n",
    "\n",
    "no_skill = len(obs[obs==1]) / len(obs)\n",
    "plt.axhline(y=no_skill)\n",
    "\n",
    "plt.xlim((0,1))\n",
    "plt.ylim((0,1.1))\n",
    "\n",
    "# fig.savefig('../figure/Fig4_pr_curve_{}_{}.svg'.format(dosage_used, cutoff))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": false,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
